03.2 Animating a Time-Dependent Function with Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Animating a Time-Dependent Function with Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For classical waves or wave mechanics, you may want to animate a time-dependent function. The following resources may be helpful in using Matplotlib for animations.\n",
    "\n",
    "[Matplotlib Animation Tutorial (2012)](http://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial/)\n",
    "\n",
    "[Animation Not Appearing in Jupyter](http://stackoverflow.com/questions/25333732/matplotlib-animation-not-working-in-ipython-notebook-blank-plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Import packages and use a magic command to display the graph in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#the \"nbagg\" parameter fixed a problem with the animation not displaying\n",
    "%matplotlib nbagg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Animation of a traveling wave.\n",
    "\n",
    "I copied this code from the Animation Tutorial referenced above. I used it to test animation in Jupyter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "                        'ui-button-icon-only');\n",
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       "    var fmt_picker = $('<select/>');\n",
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       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
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       "    nav_element.append(status_bar);\n",
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       "\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",
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       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
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       "    fig.ondownload(fig, format);\n",
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       "\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]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
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       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
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       "        cursor = 'default';\n",
       "        break;\n",
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       "        cursor = 'crosshair';\n",
       "        break;\n",
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       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             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",
       "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";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.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 overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // 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",
       "\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"639.9999861283738\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First set up the figure, the axis, and the plot element we want to animate\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(xlim=(0, 2), ylim=(-2, 2))\n",
    "line, = ax.plot([], [], lw=2)\n",
    "\n",
    "# initialization function: plot the background of each frame\n",
    "def init():\n",
    "    line.set_data([], [])\n",
    "    return line,\n",
    "\n",
    "# animation function.  This is called sequentially\n",
    "def animate(i):\n",
    "    x = np.linspace(0, 2, 1000)\n",
    "    y = np.sin(2 * np.pi * (x - 0.01 * i))\n",
    "    line.set_data(x, y)\n",
    "    return line,\n",
    "\n",
    "# call the animator.  blit=True means only re-draw the parts that have changed.\n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
    "                               frames=200, interval=20, blit=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}