Stone Soup Orbital UKF

Stone Soup Orbital UKF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An example Stone Soup notebook demonstrating inference using an unscented Kalman filter on an orbital scenario "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook simulates a nearly-Keplerian orbit and a 'sensor' which is capable of measuring the 6d Cartesian \n",
    "position/velocity vector with additive Gaussian noise. Inference is done using an unscented Kalman filter. The\n",
    "output is plotted. No metrics are generated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start with some necessary imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stonesoup.types.array import StateVector, CovarianceMatrix, StateVectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the initial time (chosen for no particular reason) and time increment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = datetime(2011, 11, 12, 13, 45, 31)  # No reason\n",
    "deltat = timedelta(seconds=60) # Propagate in discrete time at minute intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ground truth "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialise a \"ground-truth\" state vector. It's value isn't tremendously important, just note that it's \n",
    "defined in 6-d Cartesian (ECI) coordinates [km, km/s] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stonesoup.types.orbitalstate import OrbitalState, GaussianOrbitalState\n",
    "ini_cart = StateVector([[7000], [-12124], [0], [2.6679], [4.621], [0]])\n",
    "# Adjust the gravitational parameter because we're in km, rather than m\n",
    "gtstate = OrbitalState(ini_cart, timestamp=start_time, coordinates=\"Cartesian\", grav_parameter = 398600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialise a transition model which will serve to generate the ground truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stonesoup.models.transition.orbital.orbit import CartesianKeplerianTransitionModel, SGP4TransitionModel\n",
    "propagator = CartesianKeplerianTransitionModel(process_noise = CovarianceMatrix(np.diag([1e-8, 1e-8, 1e-8, 1e-10, 1e-10, 1e-10])))\n",
    "#propagator = SGP4TransitionModel(process_noise = CovarianceMatrix(np.diag([1e-8, 1e-8, 1e-8, 1e-10, 1e-10, 1e-10])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll assume that we can measure the 6-d state directly subject to some additive Gaussian noise. Define the \n",
    "matrix which will generate this noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sensor_noise = CovarianceMatrix(np.diag([100, 100, 100, 1, 1, 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialise arrays which we will use for plotting ground truth and measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_xy = np.array([[gtstate.cartesian_state_vector[0], gtstate.cartesian_state_vector[1]]])\n",
    "sens_xy = np.empty([0,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run a loop to generate the ground truth state and sensed state. Store these for later. The simulation runs in \n",
    "minute increments for a total of 1 hour. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import multivariate_normal # To generate noise\n",
    "gtstates = [gtstate]\n",
    "sensed_states = []\n",
    "time = start_time\n",
    "\n",
    "while time < start_time + timedelta(seconds=3600):\n",
    "    # Do the propagation with noise\n",
    "    gtstatevector = propagator.transition(gtstates[-1], noise=True, time_interval=deltat)\n",
    "    # Increment the time\n",
    "    time = time + deltat \n",
    "    # Add new state to states to date\n",
    "    gtstates.append(OrbitalState(gtstatevector, timestamp=time, grav_parameter=398600))\n",
    "    # Simulate a measurement\n",
    "    sensed_state_vector = gtstates[-1].state_vector + \\\n",
    "        np.array([multivariate_normal.rvs(mean=np.zeros(6), cov=sensor_noise)]).T\n",
    "    # Add this measurement to the collection of measurements\n",
    "    sensed_state = OrbitalState(sensed_state_vector, timestamp=time, grav_parameter=398600)\n",
    "    sensed_state.measurement_model = None # required by Kalman filtering routines\n",
    "    sensed_states.append(sensed_state)\n",
    "    # Put the state vector x,y Cartesian coordinates into vectors for plotting\n",
    "    all_xy = np.append(all_xy, np.array([[gtstatevector[0], gtstatevector[1]]]), axis=0)\n",
    "    sens_xy = np.append(sens_xy, \n",
    "                        np.array([[sensed_state_vector[0], \n",
    "                                  sensed_state_vector[1]]]), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a simple Earth radius by way of a parametric equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "th = np.arange(0, 2*np.pi, np.pi/100)\n",
    "xx = 6371*np.cos(th)\n",
    "yy = 6371*np.sin(th)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And do the plotting. Ground truth is red with the markers denoting the times at which observations are made, \n",
    "sensor measurements are shown in green. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xx,yy,color=\"blue\") \n",
    "plt.plot(all_xy[:,0], all_xy[:,1], color=\"red\", marker=\"+\") # Just about\n",
    "plt.plot(sens_xy[:,0], sens_xy[:,1],'x', color=\"green\")\n",
    "plt.gcf().gca().axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Prepare the Stone Soup UKF components. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a prior state vector - one quite close to the initial value. Remember that the ground truth was $[ 7000 \\mathrm{km}, -12124 \\mathrm{km}, 0 \\mathrm{km}, 2.6679 \\mathrm{km}\\mathrm{s}^{-1}, 4.621 \\mathrm{km}\\mathrm{s}^{-1}, 0 \\mathrm{km}\\mathrm{s}^{-1}]^T$. In order to make the Kalman components work we need to parameterise this state as MVN-distributed. So initialise a `GaussianOrbitalState` with a covariance matrix reflecting some (moderately sensible but large) uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ini_cart = StateVector([[7020], [-12114], [0], [2.669], [4.615], [0]])\n",
    "prior = GaussianOrbitalState(ini_cart, CovarianceMatrix(np.diag([200, 200, 200, 2, 2, 2])), timestamp=start_time, coordinates=\"Cartesian\", \n",
    "                             grav_parameter = 398600)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the Cartesian propagator as our transition model but give it a larger process noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "transition_model = propagator\n",
    "transition_model.process_noise = CovarianceMatrix(np.diag([10, 10, 10, 1, 1, 1])) # per sec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a linear Gaussian measurement model that can 'see' the full state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stonesoup.models.measurement.linear import LinearGaussian\n",
    "measurement_model = LinearGaussian(\n",
    "    6, # Number of state dimensions (position and velocity in 2D)\n",
    "    (0,1,2,3,4,5), # Mapping measurement vector index to state index\n",
    "    CovarianceMatrix(np.diag([100, 100, 100, 1, 1, 1]))\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictor and updater"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stonesoup.predictor.kalman import UnscentedKalmanPredictor\n",
    "predictor = UnscentedKalmanPredictor(transition_model)\n",
    "\n",
    "from stonesoup.updater.kalman import UnscentedKalmanUpdater\n",
    "updater = UnscentedKalmanUpdater(measurement_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cycle through the stored sensed states, operate the predict/update cycle and add to track object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stonesoup.types.hypothesis import SingleHypothesis\n",
    "from stonesoup.types.track import Track\n",
    "\n",
    "track = Track()\n",
    "for sensed_state in sensed_states:\n",
    "    prediction_G = predictor.predict(prior, sensed_state.timestamp)   \n",
    "    prediction = GaussianOrbitalState(prediction_G.state_vector, prediction_G.covar, \n",
    "                              timestamp=sensed_state.timestamp, grav_parameter=prior.grav_parameter)\n",
    "    hypothesis = SingleHypothesis(prediction, sensed_state) # Used to group a prediction and measurement together\n",
    "    post = updater.update(hypothesis) # This is a gaussian state\n",
    "    posteriorstate = GaussianOrbitalState(post.state_vector, post.covar, timestamp=sensed_state.timestamp, \n",
    "                                  grav_parameter=prediction.grav_parameter)\n",
    "    track.append(posteriorstate)\n",
    "    prior = track[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collect everything together and plot. The magenta line is now the inferred orbit, the markers and light blue \n",
    "ellipses denote the inferred state and uncertainties at the measurement times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_xlabel(\"$X$\")\n",
    "ax.set_ylabel(\"$Y$\")\n",
    "ax.axis('equal')\n",
    "\n",
    "from matplotlib.patches import Ellipse\n",
    "\n",
    "HH = np.array([[ 1.,  0.,  0.,  0., 0., 0.],\n",
    "               [ 0.,  1.,  0.,  0., 0., 0.]])\n",
    "xxx=[]\n",
    "yyy=[]\n",
    "for state in track:\n",
    "    m = HH@state.state_vector\n",
    "    xxx.append(m[0])\n",
    "    yyy.append(m[1])\n",
    "    \n",
    "# Some re-plots\n",
    "line1, = ax.plot(xx,yy,color=\"blue\") \n",
    "line2, = ax.plot(all_xy[:,0], all_xy[:,1], color=\"red\", marker=\"+\")\n",
    "line3, = ax.plot(sens_xy[:,0], sens_xy[:,1],'x', color=\"green\")\n",
    "line4, = ax.plot(xxx,yyy, color='magenta', marker=\"+\")\n",
    "\n",
    "for state in track:\n",
    "    w, v = np.linalg.eig(HH@state.covar@HH.T)\n",
    "    max_ind = np.argmax(w)\n",
    "    min_ind = np.argmin(w)\n",
    "    orient = np.arctan2(v[1,max_ind], v[0,max_ind])\n",
    "    ellipse = Ellipse(xy=(state.state_vector[0], state.state_vector[1]),\n",
    "                      width=2*np.sqrt(w[max_ind]), height=2*np.sqrt(w[min_ind]),\n",
    "                      angle=np.rad2deg(orient),\n",
    "                      alpha=0.2)\n",
    "    ax.add_artist(ellipse)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}