diff --git a/posts/duffing.ipynb b/posts/duffing.ipynb
index f171248..1f45b1c 100644
--- a/posts/duffing.ipynb
+++ b/posts/duffing.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2020-12-29T11:27:25.734Z",
@@ -22,7 +22,6 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib notebook\n",
     "import numpy as np\n",
     "from scipy.integrate import solve_ivp\n",
     "import matplotlib.pyplot as plt\n",
@@ -31,10 +30,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
+    "alpha = -1\n",
+    "beta = 1\n",
+    "gamma = 0.1\n",
+    "omega = 1.4\n",
+    "# interesting values for epsilon: 0.1, 0.4, 1.0\n",
+    "epsilon = 0.4\n",
+    "\n",
+    "\n",
     "def V(x):\n",
     "    return alpha * x ** 2 / 2 + beta * x ** 4 / 4\n",
     "\n",
@@ -47,26 +54,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.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\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.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    if (this.ratio !== 1) {\n        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n    }\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 (width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        return function (event) {\n            return fig.mouse_event(event, name);\n        };\n    }\n\n    rubberband_canvas.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband_canvas.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    rubberband_canvas.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    var cursor = msg['cursor'];\n    switch (cursor) {\n        case 0:\n            cursor = 'pointer';\n            break;\n        case 1:\n            cursor = 'default';\n            break;\n        case 2:\n            cursor = 'crosshair';\n            break;\n        case 3:\n            cursor = 'move';\n            break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.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\nmpl.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\nmpl.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.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = 'image/png';\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e) {\n        e = window.event;\n    }\n    if (e.target) {\n        targ = e.target;\n    } else if (e.srcElement) {\n        targ = e.srcElement;\n    }\n    if (targ.nodeType === 3) {\n        // defeat Safari bug\n        targ = targ.parentNode;\n    }\n\n    // pageX,Y are the mouse positions relative to the document\n    var boundingRect = targ.getBoundingClientRect();\n    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n    return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction 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\nmpl.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\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.which === this._key) {\n            return;\n        } else {\n            this._key = event.which;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.which !== 17) {\n        value += 'ctrl+';\n    }\n    if (event.altKey && event.which !== 18) {\n        value += 'alt+';\n    }\n    if (event.shiftKey && event.which !== 16) {\n        value += 'shift+';\n    }\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.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\nmpl.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\nvar _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\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data']);\n    });\n    return ws;\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.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\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager) {\n        manager = IPython.keyboard_manager;\n    }\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.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.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
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\" 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+      "image/png": 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",
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 640x480 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -75,16 +70,10 @@
    ],
    "source": [
     "# solve equation of motion\n",
-    "alpha = -1\n",
-    "beta = 1\n",
-    "gamma = 0.1\n",
-    "omega = 1.4\n",
-    "# interesting values for epsilon: 0.1, 0.4, 1.0\n",
-    "epsilon = 0.4\n",
     "\n",
     "x0, v0 = 0, 0\n",
     "n_samples_per_period = 50\n",
-    "n_period = 5000\n",
+    "n_period = 100\n",
     "period = 2 * np.pi / omega\n",
     "tmax = n_period * period\n",
     "\n",
@@ -137,33 +126,33 @@
     "plt.tight_layout()\n",
     "\n",
     "def animate(i):    \n",
-    "    ln1.set_data(x[i], V(x[i]))\n",
+    "    ln1.set_data([x[i]], [V(x[i])])\n",
     "    j = max(0, i - 10 * n_samples_per_period)\n",
     "    sl = slice(j, i+1)\n",
     "    ln2.set_data(t[sl], x[sl])\n",
     "    ax2.set_xlim(t[j], t[i+1])\n",
     "    sl = slice(0, i+1)\n",
     "    ln3.set_data(x[sl], xdot[sl])\n",
-    "    ln3a.set_data(x[i], xdot[i])\n",
+    "    ln3a.set_data([x[i]], [xdot[i]])\n",
     "    return (ln1, ln2, ln3, ln3a)\n",
     "\n",
     "anim = animation.FuncAnimation(fig, animate, frames=len(x), interval=2)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "To see the animation in action, run this code in a Jupyter notebook and prepend the `%matplotlib notebook` to the first cell."
+   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "py38",
+   "display_name": ".venv",
    "language": "python",
-   "name": "py38"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -175,7 +164,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.3"
   },
   "nteract": {
    "version": "0.27.0"