modeling.ipynb

{
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   "cell_type": "code",
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   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import minimize, least_squares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "In this notebook, you will learn how to fit light curves of gravitational microlensing events. Microlensing occurs when the light from a distant star (known as a source) is bent and magnified in the gravitational field of an intervening lensing object (lens). An observer on Earth can detect a brief transient brightening of the otherwise constant source star. Microlensing events are detected mostly toward the Galactic center and disk.\n",
    "\n",
    "In this tutorial, we will model the light curve of microlensing event ZTF19aavisrq that was detected by ZTF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading data from a text file\n",
    "\n",
    "time, mag, magerr = np.loadtxt('img/ZTF19aavisrq.dat', unpack=True, usecols=(0, 1, 2))\n",
    "time -= 2450000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "            break;\n",
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       "    }\n",
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       "\n",
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       "    // Called whenever the canvas gets updated.\n",
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       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.errorbar(time, mag, magerr, fmt='o', mfc='none', color='k')\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlabel('HJD-2450000 (days)')\n",
    "ax.set_ylabel('Magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every microlensing event has a characteristic angular scale, which we call the angular Einstein radius $\\theta_E$. The Einstein radius depends on the mass of the lens $M$, the distance of the lens $d_L$, and the distance to the source $d_S$:\n",
    "$$\n",
    "\\theta_E = \\sqrt{\\frac{4GM}{c^2}\\frac{d_S-d_L}{d_S d_L}}.\n",
    "$$\n",
    "Calculate the Einstein radius for a typical lens that has a mass of $0.5\\,M_{\\odot}$ and is located 4 kpc from the observer. Assume that the source star is located in the Galactic bulge at 8 kpc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the simplest case, when the lens is an isolated object and the source star can be regarded as a point, the magnification of the source depends on the lens-source separation $\\theta$ scaled to $\\theta_E$:\n",
    "$$\n",
    "A(u) = \\frac{u^2+2}{u\\sqrt{u^2+4}},\n",
    "$$\n",
    "where $u=\\theta/\\theta_E$. This function has several important properties. $A(u)$ is always greater than 1, so microlensing can only increase the brightness of the source star, not decrease it. $A(u)$ always decreases as $u$ increases, so the closer the alignment, the brighter the source becomes. As $u$ approaches infinity, $A(u)$ approaches 1, so that at wide separations, microlensing has no effect. Finally, as $u$ approaches 0, for a point source $A(u)$ approaches infinity as the images approach an Einstein ring. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lens-source separation $u$ varies with time. The function $u(t)$ can be determined by the Pythagorean theorem:\n",
    "$$\n",
    "u(t) = \\sqrt{u_0^2 + \\tau(t)^2} = \\sqrt{u_0^2+\\left(\\frac{t-t_0}{t_E}\\right)^2},\n",
    "$$\n",
    "where $u_0$ is the minimum lens-source separation, $t_0$ is the time of the peak, and $t_E$ is called the Einstein time. $t_E$ is the time it takes the lens to transverse an angular distance $\\theta_E$ relative to the source."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/geom_Aofu.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The amplitude of the event depends on the impact parameter $u_0$. Plot the function $u_0=u_0(A_{\\rm max})$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our lensing model has three parameters: $t_0$ -- time of the peak, $t_E$ -- Einstein timescale, and $u_0$ -- impact parameter. There are two auxialiary parameters: $F_s$ -- flux of the source star (that is magnified during the event) and $F_b$ -- blended flux, which may come from unrelated unresolved stars and/or the lens itself. The blend flux is not magnified during the event. The total flux is:\n",
    "$$\n",
    "F(t) = F_s A(t; t_0,t_E,u_0) + F_b.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will find the best-fit parameters by minimizing the function $\\chi^2$, which is the sum of squares of residuals between the observations and model predictions:\n",
    "$$\n",
    "\\chi^2 = \\sum_{i=1}^N \\frac{(F_i-F(t_i))^2}{\\sigma_i^2} = \\sum_{i=1}^N \\frac{(F_i-F_s A(t_i;t_0,t_E,u_0)-F_b)^2}{\\sigma_i^2}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model is linear in $F_s$ and $F_b$. This means that for every set of $(t_0,t_E,u_0)$, we can directly find $F_s$ and $F_b$ that minimize $\\chi^2$. We require:\n",
    "$$\n",
    "\\frac{\\partial\\chi^2}{\\partial F_s} = 0\\ \\mathrm{and} \\ \\frac{\\partial\\chi^2}{\\partial F_b} = 0.\n",
    "$$\n",
    "\n",
    "Thus (you can prove that!):\n",
    "$$\n",
    "F_s \\sum_{i=1}^N \\frac{A(t_i)^2}{\\sigma_i^2} + F_b \\sum_{i=1}^N \\frac{A(t_i)}{\\sigma_i^2} = \\sum_{i=1}^N \\frac{A(t_i)F_i}{\\sigma_i^2}\\\\\n",
    "F_s \\sum_{i=1}^N \\frac{A(t_i)}{\\sigma_i^2} + F_b \\sum_{i=1}^N \\frac{1}{\\sigma_i^2} = \\sum_{i=1}^N \\frac{F_i}{\\sigma_i^2}\\\\\n",
    "$$\n",
    "\n",
    "We now have all ingredients needed to code the function $\\chi^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def amplification(params, time):\n",
    "    assert len(params) == 3\n",
    "    t_0, t_E, u_0 = params\n",
    "    tau = (time-t_0)/t_E\n",
    "    u = np.sqrt(tau*tau + u_0*u_0)\n",
    "    A = (u*u+2.0)/u/np.sqrt(u*u+4.0)\n",
    "    return A\n",
    "\n",
    "def get_fluxes(params, time, flux, flux_err):\n",
    "    A = amplification(params, time)\n",
    "    weight = 1.0/(flux_err*flux_err)\n",
    "    sum1 = np.sum(A*A*weight)\n",
    "    sum2 = np.sum(A*weight)\n",
    "    sum3 = np.sum(weight)\n",
    "    sum4 = np.sum(A*flux*weight)\n",
    "    sum5 = np.sum(flux*weight)\n",
    "    F = np.linalg.solve([[sum1, sum2], [sum2, sum3]], [sum4, sum5])\n",
    "    residuals = (flux-F[0]*A-F[1])/flux_err\n",
    "    chi_squared = np.sum(residuals*residuals)\n",
    "    return chi_squared, F, residuals\n",
    "\n",
    "def chi2(params, time, flux, flux_err):\n",
    "    chi_squared, F, residuals = get_fluxes(params, time, flux, flux_err)\n",
    "    return chi_squared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\chi^2$ minimization with the Nelder-Mead algorithm\n",
    "Let's fit some data! First, we need to convert magnitudes to flux. We use the Pogson law and assume that a unit flux corresponds to a 18 mag object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "flux = pow(10.0, 0.4*(18.0-mag))\n",
    "flux_err = 0.4*np.log(10.0)*flux*magerr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now use scipy's minimize function to find ($t_0,t_E,u_0$) that minimize our $\\chi^2$ function. We have to choose initial guess parameters. For example, for $t_0=8652$, $t_E=50$ days and $u_0=0.05$, we get $\\chi^2=1233.2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1233.1875593077625\n"
     ]
    }
   ],
   "source": [
    "params0 = np.array([8652., 50., 0.05])\n",
    "chi_sq = chi2(params0, time, flux, flux_err)\n",
    "print(chi_sq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " final_simplex: (array([[8.65181355e+03, 8.91671423e+01, 1.78801632e-02],\n",
      "       [8.65181355e+03, 8.91672147e+01, 1.78801449e-02],\n",
      "       [8.65181355e+03, 8.91671120e+01, 1.78801730e-02],\n",
      "       [8.65181355e+03, 8.91671877e+01, 1.78801450e-02]]), array([464.11562406, 464.11562406, 464.11562406, 464.11562406]))\n",
      "           fun: 464.11562405801016\n",
      "       message: 'Optimization terminated successfully.'\n",
      "          nfev: 237\n",
      "           nit: 127\n",
      "        status: 0\n",
      "       success: True\n",
      "             x: array([8.65181355e+03, 8.91671423e+01, 1.78801632e-02])\n"
     ]
    }
   ],
   "source": [
    "res = minimize(\n",
    "    chi2,  # function to be minimized\n",
    "    params0,  # initial guess\n",
    "    args=(time, flux, flux_err),  # extra arguments passed to the objective function\n",
    "    method='Nelder-Mead',  # minimization algorithm\n",
    ")\n",
    "\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the best-fitting model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.10011119 0.56057475]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get source and blend fluxes:\n",
    "chi_sq, F, residuals = get_fluxes(res.x, time, flux, flux_err)\n",
    "print(F)\n",
    "\n",
    "time_model = np.linspace(time[0], time[-1], 5000)\n",
    "A_model = amplification(res.x, time_model)\n",
    "flux_model = F[0]*A_model + F[1]\n",
    "mag_model = 18.0 - 2.5*np.log10(flux_model)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.errorbar(time, mag, magerr, fmt='o', mfc='none', color='k')\n",
    "ax.plot(time_model, mag_model, color='blue')\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlabel('HJD-2450000 (days)')\n",
    "ax.set_ylabel('Magnitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using least-squares methods\n",
    "The minimization can be also performed using the least squares method. We'll use the Levenberg-Marquardt algorithm as an example, but we have to modify our objective function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " active_mask: array([0, 0, 0])\n",
      "        cost: 232.05781202905908\n",
      "         fun: array([-2.25523818e+00,  8.87369774e-01,  8.87252238e-01, -4.33460205e-01,\n",
      "        7.53111780e-01, -1.26615284e-01, -1.84536536e-01, -1.71366246e+00,\n",
      "       -8.11648604e-01,  9.38636951e-01,  8.97115167e-02,  6.15811103e-02,\n",
      "        2.62714218e+00, -4.70170375e-01,  3.00874627e-02, -1.90826647e-01,\n",
      "        7.95239790e-01, -1.17421693e-01, -3.88273843e-01, -3.32723509e-01,\n",
      "        1.06752424e+00, -2.51872974e-01, -1.29932828e+00, -8.51362621e-01,\n",
      "       -2.61645496e+00, -2.10784172e+00,  1.28483464e+00,  1.07941177e-01,\n",
      "       -8.81023948e-01, -1.07687049e+00,  6.27969747e-01,  2.98402861e-01,\n",
      "       -2.85151348e-01, -3.52729945e-02,  1.05910885e+00, -3.52021773e-01,\n",
      "        2.03403865e+00,  7.30405490e-01, -1.02835209e+00, -1.50764915e-01,\n",
      "        3.74612089e-01, -4.03936714e-01,  2.71852288e+00, -7.02969218e-01,\n",
      "       -7.79390014e-01,  1.60071338e+00,  4.18649667e-02,  1.10801442e+00,\n",
      "        6.78603993e-01,  3.84481631e-01, -8.64567930e-01,  1.51750286e+00,\n",
      "       -3.57996885e-01, -9.83166271e-01,  1.27048824e+00, -6.92582635e-01,\n",
      "        2.58053716e-01,  1.59498429e+00, -7.86785688e-01,  1.80950140e+00,\n",
      "        3.65950471e-01, -2.43904313e-01, -6.66856085e-01,  1.40312595e+00,\n",
      "        1.17954946e-01,  9.35730606e-01,  3.11014863e-01, -1.04217210e-01,\n",
      "       -4.85215088e-02, -1.03808552e+00,  7.20547202e-01, -4.68756289e-01,\n",
      "        6.66267954e-01,  9.02684246e-02, -1.43238880e+00, -4.40561854e-01,\n",
      "        1.17948767e-01, -6.10132318e-01, -9.54256652e-01, -4.96983002e-01,\n",
      "        1.15658495e+00,  1.43038336e+00,  4.75499720e-01,  7.06444445e-03,\n",
      "       -1.91807372e+00,  4.48145771e-01, -4.40567631e-01,  5.57403450e-01,\n",
      "       -1.01012515e+00,  3.48198322e-02, -1.32115764e-01,  6.25517326e-02,\n",
      "       -6.66871946e-01, -4.12400722e-01,  7.96848117e-01, -2.07258280e-02,\n",
      "       -7.87292171e-01,  1.32115816e+00,  6.39078150e-01,  4.75491062e-01,\n",
      "       -1.34748095e+00,  5.30119360e-01, -1.60034387e-01,  1.17934698e-01,\n",
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      "        1.17898402e-01,  2.55925773e-01,  1.23894066e+00,  9.02149188e-02,\n",
      "        6.11836048e-01,  1.10147122e+00,  5.30079369e-01, -4.85799293e-02,\n",
      "        1.01868455e+00,  6.11823055e-01, -6.39043682e-01,  1.45059771e-01,\n",
      "        1.15604380e+00, -1.04772867e-01,  5.84121396e-01, -1.37631032e+00,\n",
      "        6.38549785e-01,  1.10097663e+00, -1.29154511e+00,  5.02286468e-01,\n",
      "       -7.52724301e-01, -4.90803567e-02,  1.72684965e-01,  3.42723314e-02,\n",
      "        7.96300874e-01,  1.10097246e+00, -7.24269566e-01,  2.82954031e-01,\n",
      "        7.96298769e-01, -6.10701089e-01, -1.60582293e-01, -1.32673520e-01,\n",
      "        2.00282277e-01,  2.55417578e-01, -4.69331819e-01,  7.96291570e-01,\n",
      "       -4.12964726e-01, -7.52737044e-01, -9.26930865e-01, -1.32677740e-01,\n",
      "        3.65380859e-01, -5.82384415e-01,  5.02270624e-01, -3.00543690e-01,\n",
      "        8.96985854e-02, -3.00544916e-01,  6.11327988e-01, -1.60595721e-01,\n",
      "       -7.52744114e-01,  8.96952586e-02, -1.43297572e+00, -1.86113002e+00,\n",
      "       -5.82391974e-01, -4.41147411e-01, -7.69398678e-02, -2.12087933e+00,\n",
      "       -1.43298082e+00,  7.40548952e-01, -4.91072037e-02, -1.66059544e+00,\n",
      "        6.19786336e-02,  8.96859095e-02, -5.54098501e-01, -2.16510670e-01,\n",
      "       -6.95866466e-01,  1.10093008e+00,  6.65680007e-01, -4.91263034e-02,\n",
      "        1.10092852e+00,  4.47558549e-01, -6.39098567e-01, -5.82418507e-01,\n",
      "        8.96642734e-02, -1.43300717e+00,  5.56814764e-01,  1.42979133e+00,\n",
      "       -2.16533455e-01,  9.62816772e-01, -1.29160132e+00, -3.00582745e-01,\n",
      "        5.29534167e-01, -2.72542092e-01,  1.72629947e-01, -5.25850774e-01,\n",
      "       -4.91373519e-02, -2.13276549e-02,  8.96569355e-02, -6.10756476e-01,\n",
      "       -4.41184148e-01,  8.89920315e-01,  4.73739980e-03, -8.74129004e-01,\n",
      "       -1.86479302e+00,  1.26776655e-01,  5.79422925e-01,  1.35223613e+00,\n",
      "       -5.31921830e-01,  1.61408864e+00,  1.78663110e+00,  3.03848621e-01,\n",
      "       -1.25504996e-01, -5.05029338e-01,  8.84481196e-01, -1.02117785e+00,\n",
      "       -5.27031092e-02,  6.27966919e-01,  1.41856596e+00,  1.30574403e-01,\n",
      "       -9.30499110e-02,  1.01957124e+00,  4.47066394e-01, -5.83966053e-01,\n",
      "       -7.83859177e-01, -1.09167809e+00,  1.04233339e+00,  9.58281106e-01,\n",
      "        4.96708687e-01,  6.22666932e-01, -3.07465576e-01,  2.96290816e-01,\n",
      "       -1.19208073e+00, -2.03663481e-01,  3.13589001e-01,  1.27631167e+00,\n",
      "       -3.35733314e+00,  1.07528572e+00,  1.26497789e+00,  2.02196290e+00,\n",
      "        2.73738863e-01,  2.68593717e+00,  1.13020763e+00,  1.86594610e+00,\n",
      "        1.42455933e+00,  6.71483514e-01,  7.97460597e-01,  3.40685417e-01,\n",
      "        2.47034890e+00,  1.20599822e+00,  3.67536745e-01,  9.09745291e-01,\n",
      "        3.26606996e-01,  6.33117881e-01,  2.36581118e-01,  3.52899787e-01,\n",
      "        1.17070436e+00,  1.53762748e+00, -1.31866058e+00,  2.18899345e-03,\n",
      "        1.12901449e+00,  3.18141324e-01, -8.03451957e-01,  5.69077566e-01,\n",
      "        1.42698697e+00, -1.44771457e+00,  3.26707705e-02, -2.33454998e-01,\n",
      "        1.68111315e-01,  1.79824652e+00,  3.28311538e-02, -1.56258306e-01,\n",
      "       -2.81215894e-01, -1.36106140e-01,  5.60182075e-01, -8.07796597e-01,\n",
      "        1.72853594e-01, -3.98622802e-02, -1.32343592e-01,  8.42566289e-01,\n",
      "        4.66925677e-01, -7.70163843e-02,  6.36061869e-01, -2.08925433e-01,\n",
      "        4.91927963e-03, -9.95295361e-01, -1.10506390e+00, -1.40812332e+00,\n",
      "       -1.81253609e+00, -6.61909754e-01, -7.70901228e-02,  1.48342297e-01,\n",
      "        9.21952761e-02, -1.38663412e+00, -3.45585519e-01, -4.22931861e-01,\n",
      "        8.37596586e-01,  1.20040619e-02, -1.01067560e+00,  1.63021546e+00,\n",
      "       -1.12049351e+00, -8.17361382e-01, -3.26835682e-01,  6.74957164e-03,\n",
      "        1.19003876e+00,  8.73923646e-01,  1.72200154e+00,  8.26529071e-01,\n",
      "        3.31036989e-01, -1.70394410e-01, -1.18788717e+00, -2.41952404e-01,\n",
      "       -6.86344322e-01, -4.07417163e-01, -9.14532443e-01, -1.56549018e+00,\n",
      "       -8.81828202e-02, -7.72153716e-01, -6.84428255e-01,  3.03183580e-01,\n",
      "        9.16680067e-02,  5.83781721e-01,  1.52296903e-01, -3.28928794e-01,\n",
      "       -6.65935879e-01, -9.47970068e-01, -4.69007183e-02, -2.58764148e-01,\n",
      "       -3.02590007e-01, -6.44964253e-01, -7.64033446e-01, -3.92906261e-01,\n",
      "        1.12940276e+00,  1.80183694e-01, -1.04064487e+00, -8.22748703e-01,\n",
      "       -2.83108725e-01, -7.42309344e-02, -1.57345831e-01, -3.04447994e-01,\n",
      "       -1.30286067e+00, -1.53856876e-01, -1.51068788e+00,  3.61567925e-02,\n",
      "       -7.48981851e-01, -5.67926472e-01,  2.36571505e-01, -8.75634210e-01,\n",
      "       -5.73927644e-01,  4.30988304e-01,  2.14413079e-01, -1.03574107e+00,\n",
      "       -9.75927114e-01, -5.98475509e-01,  8.04199698e-01,  1.68451541e-01,\n",
      "       -1.39134325e-01,  3.19733266e-01,  2.14275341e+00, -4.42743364e-01,\n",
      "       -8.96894787e-01,  1.83969217e-01,  8.35499320e-01, -2.67440373e-01,\n",
      "        1.16403577e+00, -1.02501515e-01, -9.20860667e-01,  7.72251615e-02,\n",
      "       -1.16867466e+00,  1.35908828e+00,  5.89283943e-01,  8.46224645e-01,\n",
      "       -6.98257368e-01,  6.02627397e-01,  1.33981903e+00, -1.90991472e+00,\n",
      "        4.98946791e-01,  1.32948459e+00,  7.75514828e-01,  2.32493526e-01,\n",
      "       -6.75328260e-01,  1.49424835e+00,  9.47857493e-01, -4.59858969e-01,\n",
      "       -1.28974448e+00, -1.10448623e+00, -1.53509014e+00,  4.27952147e-01,\n",
      "        8.04059291e-01,  3.83931161e-01, -1.44822682e+00, -1.70754356e+00,\n",
      "        1.15816018e+00, -5.88486458e-01, -6.14099571e-01, -6.16318791e+00,\n",
      "        2.42126839e-01, -1.50711926e+00,  1.07568901e+00,  1.90171324e+00,\n",
      "        1.73892600e+00,  1.17045175e+00,  1.02266928e+00,  1.52247725e+00,\n",
      "       -1.15676216e-01,  4.68110359e-02, -1.49540327e+00, -2.21388156e+00,\n",
      "       -4.31181468e-01, -1.78193073e-01, -9.16618768e-01,  9.73102373e-01,\n",
      "        2.27297601e+00,  4.03782848e-01, -8.04078574e-01,  1.03993453e+00,\n",
      "        9.69796383e-01,  2.62454293e-01, -1.05949205e+00, -4.33821568e-01,\n",
      "        7.54491822e-01, -1.33996192e+00,  9.76210945e-02, -5.74183345e-01,\n",
      "        3.24978300e+00, -1.52274293e-01,  1.05447230e+00,  3.61790582e+00,\n",
      "       -7.50870599e-01, -4.02890443e-01,  1.19345232e+00, -7.22381637e-01,\n",
      "        6.75702522e-01,  6.21440865e-01, -7.49809265e-01, -7.13621336e-01,\n",
      "       -9.72003144e-01,  1.52258833e+00, -1.64890903e+00,  1.16735383e+00,\n",
      "       -4.57632875e-01,  1.00222358e+00,  1.03001844e+00,  1.02298468e-01,\n",
      "       -1.33505401e+00,  6.52915898e-01, -2.70474521e+00])\n",
      "        grad: array([ 2.86820307e-03, -4.33413140e-06, -2.34225243e-02])\n",
      "         jac: array([[7.64987693e-02, 1.42867906e-02, 3.63517319e+01],\n",
      "       [7.47752585e-02, 1.39614889e-02, 3.55312402e+01],\n",
      "       [7.47731945e-02, 1.39577052e-02, 3.55287775e+01],\n",
      "       ...,\n",
      "       [7.60970733e-02, 1.43854212e-02, 3.62889900e+01],\n",
      "       [7.32630112e-02, 1.38992393e-02, 3.49434349e+01],\n",
      "       [7.76979205e-02, 1.47672103e-02, 3.70620440e+01]])\n",
      "     message: '`ftol` termination condition is satisfied.'\n",
      "        nfev: 30\n",
      "        njev: None\n",
      "  optimality: 0.02342252430337055\n",
      "      status: 2\n",
      "     success: True\n",
      "           x: array([8.65181355e+03, 8.91672190e+01, 1.78801423e-02])\n"
     ]
    }
   ],
   "source": [
    "def get_residuals(params, time, flux, flux_err):\n",
    "    chi_squared, F, residuals = get_fluxes(params, time, flux, flux_err)\n",
    "    return residuals\n",
    "\n",
    "ls_res = least_squares(\n",
    "    get_residuals,\n",
    "    params0,\n",
    "    method='lm',\n",
    "    args=(time, flux, flux_err),\n",
    ")\n",
    "\n",
    "print(ls_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this function converges much more quickly that the $\\chi^2$ minimization algorithm (Nelder-Mead algorithm). It required only 30 evaluations of the objective function (whereas the Nelder-Mead algorithm needed 237 function evaluations!) If the calculation of the objective function is computationally costly, least-squares method are much more quicker! You can test other algorithms by changing the method argument: Trust Region Reflective algorithm (method='trf') and dogleg algorithm (method='dogbox'). You can also speed up calculations by providing the Jacobian matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uncertainties\n",
    "We will use the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler coded by Foreman-Mackey et al. to measure parameter uncertainties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import emcee\n",
    "import corner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_prior(params):\n",
    "    if params[1] < 0:\n",
    "        return -np.inf\n",
    "    if params[2] < 0:\n",
    "        return -np.inf\n",
    "    return 0.0\n",
    "\n",
    "def log_prob(params, time, flux, flux_err):\n",
    "    chi_squared, F, residuals = get_fluxes(params, time, flux, flux_err)\n",
    "    prior = log_prior(params)\n",
    "    if np.isinf(prior):\n",
    "        return -np.inf\n",
    "    return -0.5*chi_squared + prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndim = 3  # number of dimensions\n",
    "nwalkers = 100  # number of walkers\n",
    "nburnin = 3000\n",
    "nsteps = 2000\n",
    "\n",
    "p0 = np.random.multivariate_normal(res.x, cov=np.diag([1.0, 5.0, 0.00001]), size=nwalkers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = emcee.EnsembleSampler(\n",
    "    nwalkers,\n",
    "    ndim,\n",
    "    log_prob,\n",
    "    args=[time, flux, flux_err]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "state = sampler.run_mcmc(p0, nburnin)\n",
    "sampler.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "state = sampler.run_mcmc(state, nsteps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = sampler.get_chain(flat=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"760\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['$t_0$', '$t_E$ (days)', '$u_0$']\n",
    "\n",
    "corner.corner(\n",
    "    samples,\n",
    "    labels=labels,\n",
    "    quantiles=[0.16, 0.5, 0.84],\n",
    "    show_titles=True,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}