Visualize_Series_Representation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series representations of functions\n",
    "In this notebook, we look at series representations of functions and visualize functions we want to represent and the partial sums of their series representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "from scipy import integrate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing series representations\n",
    "First, we calculate the coefficients of the infinite sum representation $\\sum_{n=1}^\\infty a_n v_n(x)$ of a given function $f(x)$ defined on the interval $[0,\\pi]$ numerically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defines the function f(x)\n",
    "def f(x):\n",
    "    return x*(np.pi-x)\n",
    "\n",
    "# defines the basis functions we use to represent f(x)\n",
    "def vn(x, n):\n",
    "    if n==0:\n",
    "        # v0(x)=e^x\n",
    "        v = np.exp(x)\n",
    "    else:\n",
    "        # vn(x) = cos(nx)+sin(nx)/n\n",
    "        v = np.sin(n*x)/n+np.cos(n*x)\n",
    "    return v\n",
    "\n",
    "# computes the partial sum of the first N+1 terms\n",
    "def computeSeries(x, N):\n",
    "    def computeCoeff(n):\n",
    "        C1,D = integrate.quad(lambda x: f(x)*vn(x,n), 0, np.pi)\n",
    "        C2,D = integrate.quad(lambda x: vn(x,n)**2, 0, np.pi)\n",
    "        return C1/C2\n",
    "    fs = np.array([computeCoeff(n)*vn(x, n) for n in range(0,N+1)])\n",
    "    fs = fs.sum(axis=0)\n",
    "    return fs.real"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting functions and their series\n",
    "Next, we compute and plot the partial sums of a given function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "            /* 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\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.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",
       "\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"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.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",
       "\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.one(\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",
       "\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",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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VF5eXpn+zZo1K70nztHKLfDDDz9o6tSpuvrqq3X00UeradOmhdf41FNPLfd+S9qQz56SdKJVl67fHz5/ovV7UZZoFyxYoOuuuy72udO6dWvVrl1b9evXV4cOHWSfPXPmzCnLblJah8+elLhCvXK6fn/47An1r0HKwf3666+yz4Hhw4froIMOUrt27dSwYUPVqlVLzZo108EHH6ybbrpJP/74Y8r7Lm4DPneKk6EcgWIE8kkI5JCA1xDP9/4Q5Xv/OxT5b5dddsn/7LPPKiSyZs2a/F69ehW5fztutWrV8v/5z39W6BhsnH6Bqv7d+fvf/17s70zw9/XVV19NPwBHLJdA8Nr586ecckq59lncRnz2FCcT3XL/70twuTJ/f/j8ie7vSFGRH3jggWX6e+J9KZm/bt26onaRUhmfPSlxhX7ldP7+8NkT+l+HlAKcOXNmmT57vC9D81944YWU9h1cmc+doAh5BMomUMO7oSQhkBMC7777rvr16xd7ys++Bb/88svVo0cPeX9AYt9W3X///Vq8eLF69uyp+fPnx74pLw/M6aefHnvix7a1/V944YVq2bKl3n///di38V4HY+xpIHsycNiwYeU5BNukWSBdvzvx07LflZLSjjvuWFI1dSEVsKdw7MnjGTNmVEmEfPZUCWtodlrVvz/xE+XzJy4R3Z/ffPNNLHi79+jbt68OOOAAtWnTRps2bdIbb7yh0aNHy9YZN26cNm7cqMcee6xCJ8tnT4X4Qrdxun9/4gB89sQlov3T/lZZ+6dr166yZWvvbN68WUuXLtVTTz2lSZMmacWKFTr22GNj7a1dd921XCfM50652NgIAe9xJBICOSLgPXYe+1aqRo0a+a+//nrSWXuPpBd+a/WPf/wjqb4sBd6rmYX76N27d753Y+1stnz58nzvJjy2TqNGjfJXrlzp1JMJp0A6fnf834KHU4GoyiPgvfqb/9xzz+V/9913sc2/+OKLws+IynyCi8+e8lyd8G+Trt8fPn/C/7uQSoTeF5n5EydOTLoHie/D7kW8V4ELP4v+85//xKtS/slnT8pkod8gnb8/fPaE/tchpQCD7Z6iNp48eXLhZ0+fPn2KWqXUMj53SiViBQSKFaADsFgaKrJJYN68eYV/bM4666wiT837Zjzfezontp51zq1fv77I9UoqPOaYY2LbV69ePX/JkiVFrvr4448XxuKNO1jkOhSGRyBdvzvcBIfnmldlJFXVAchnT1VetfDsu6p+f/j8Cc81Tlck9sWE9yBE7N8FF1xQ7sPy2VNuukhvWFm/P3z2RPrXoNzBd+zYMfbZY68ClyfxuVMeNbZBoECASUB4DDQnBKZMmVJ4nkOHDi1c9i94Y/NpyJAhsSLvyTx53y75q0tdXrVqlV5++eXYeocffrhatWpV5Dbet11q0KBBrM4egyeFWyAdvzvhFiC6sAvw2RP2K0R8CIRPwAbjjycbmqQ8ic+e8qhlxzaV8fuTHRKcRXkEbBJGS2vXrk15cz53UiZjAwQcAToAHQ4y2SoQn+3O/uDYmBTFJZuxKp7mzp0bXyzTT+9JMXmDacfW9e8nuLHNhLXvvvvGim2bDRs2BFchHyKBdPzuhOh0CSWCAnz2RPCiETICGRbw3nIojMC+AC1P4rOnPGrZsU1l/P5khwRnkarAokWL9N///je2mfckYKqbi8+dlMnYAAFHoHx/8Z1dkEEg/AL2x8aSTUfvjQFYbMD+P0TxbYpdOVDhX9+/n8BqsWy83gbf/uSTT4pahbKQCMSva1X+7gRP1Z4gbdKkiayzuFmzZv+/vTsJlaPoAwBe0YCJG65EPKioCIrBHFwQFXE3GpcQEhQRBQMKBgxEUPRgLkLwEBUPRkUNYlT04AIixIgILrmouCuKoqgQ1BANJgcP83U1X48z896b1zVvZpya+TXI9PLv7qpfVcp5/+klxF/aN2zYEOKVqSYCnQJVH43rq7GlM6ZarrYbeyoRn50Cxp9OkfFcfuedd5oVq8aF5oqaM8aemlBjGNaP/tPJYuzpFBmf5T179pR/72zcuLF8QUh8IVGc4osSUyfjTqqYeALtAhKA7R6WxlAgXl4e3zYVp5luy62qXTz7L1SXpRfP8KtW1/psjZ/tPPGtWNXUul+1zudoCAyr73TWdtu2bWHnzp3l1aHFw9pD/KId31p9/PHHh1dffbUz3PKEC7SOIcaeCe8Mfai+8acPiCN+iPhGzvijUjWtWrWqmk36NPYkcY1NcL/6TyeIsadTJO/lzZs3h3nz5pX/xb+tihcPhXXr1oUdO3aUFbvzzjvDDTfckFxJ404ymR0ItAnMfClUW5gFAvkK7N69u1n4Aw88sDk/00z8n9Tff/8d4jMmUqaU81RJxnj81POklEns3ARS2jSeqde+U5Vy8eLF4dprrw1nnnlmOProo8sE4DfffBO2bNkStm7dGnbt2hVWrFgRiodvh6VLl1a7+ZxwgZR+auyZ8M7SpfrGny44Y7bpwQcfLG+ji9Vavnx5OP3003uqobGnJ7bsd+pX/6kgjD2VxGR8LlmyJGzatCmcddZZPVXYuNMTm50INAUkAJsUZsZVoPUBs/GWytmm/fbbrwzZu3fvbKFt21POU50jHiD1PG0ntTBQgZQ2jQWp2rWXNl27dm1Yv379lPrEL0jx5TSPPfZYuO2220K8bWL16tXhu+++CwsXLpwSb8XkCaT006qPRqVe+unk6U5GjY0/k9HOsZbxivK77767rHB8xMSjjz7ac+WNPT3TZbtjP/tPRDD2ZNsVZi14/EG7+nEhft+ILxt68cUXw8svv1xe+ffQQw+FZcuWzXqczgDjTqeIZQJpAm4BTvMSnaHAggULmqVufWhxc2XHTPUij9TkSsp5qnPEU6eep6O4FgcokNKmsRhVu/bSpoccckjXmtx6661l4i8G/frrr8EbpLtyTdTGlH5a9dEI1Es/nSjYCaqs8WcyGvuLL74or/iLzwCNPwbEP8YXLVrUc+WNPT3TZbljv/tPRDD2ZNkVahU6tu2pp55a/nfGGWeE6667rvzu+swzz4Tvv/8+XHPNNSHeJpw6GXdSxcQTaBeQAGz3sDSGAgcddFCzVnVut423/8apzu3CzQMXMynnqc7Ry3laz2l+sAIpbRpLUrVrat+pW4uYBKym1gdwV+t8TqZASj+t+miUGlQ/ncxWGP9aG3/ybuMffvghXHrppeXLpPbdd9/w/PPPh/PPP39OlTL2zIkvq50H0X/qAhh76krlEXfjjTeGlStXhvgsyTVr1iS/4M64k0c7K+XoCkgAjm7bKFmfBOIvRUcccUR5tJ9//rnrUeNbVqs/kFtf1NF1p/9vbH34/mznaX2Abep56pRFTH8EhtV36pb2lFNOaYb+8ssvzXkzky1g7Jns9h9W7Y0/w5Lu/3niVeMXX3xxefV4fCj/U089VV4JONczGXvmKpjH/oPqP3Vrb+ypK5VPXLz6L07xb6433ngjqeDGnSQuwQSmCEgATiGxYhwFTj755LJa8blp8daXmaavv/66uanap7lilpnWLyitx5lut2r7/Pnzw4knnjhdiHUjIlD1g0H2nbpVbTQadUPFTZCAsWeCGvs/rKrx5z/En8Opf//993DJJZeUt9zFwzzyyCPlc2XncMjmrsaeJsXYzgyy/9RFM/bUlcon7sgjj2wW9scff2zO15kx7tRREkNgZgEJwJltbBkjgXPPPbesTfyl6cMPP5yxZq23VZ5zzjkzxk23IT7fonrJSOtxOmPjcwi3b99erm7dpzPO8mgIDKPv1K3pl19+2QyNbwk2EYgCreOIsUefGJSA8WdQsoM77p9//hkuu+yyULXdhg0bwu233963Exp7+kY5kgcadP+pW+mq/8Z4333qqo12XOtdLKmPIzHujHbbKt3oC0gAjn4bKWEfBOKbqKrp6aefrmbbPuOzKOKDaeMUH1x7wQUXtG2fbSE+k+Kiiy4qw7Zt2xZmug04vrzhr7/+KuOWL18+22Ft/48FhtF36lYxvgm4mub67KbqOD7zFzD25N+GOdTA+JNDK/1bxj179oQrr7wyfPTRR+XKe++9N9x1113/BvRhztjTB8QRPcQw+k/dqht76krlE/fSSy81C7t48eLmfJ0Z404dJTEEuggUl1WbCEyEwHnnnRfvn2wUt9023n///Sl1fuCBB8rtMea+++6bsr1IHHbdHnd46623mjFXX311o7jduO04v/32W+OYY44pY4okY2Pnzp1t2y2MpsCg+86nn37a+Pbbb7tWftOmTc2+ddRRRzWKF9p0jbdxNAWKB6k32/Gmm26qVUhjTy2miQgaRP8x/oxf1yne9t0oXvjRHGvuuOOOnipp7OmJLfudhtV/jD3Zd5UpFYhjxt69e6esb12xcePG5th03HHHNf7555/WzQ3jThuHBQJ9F5jfJTdoE4GxEnj44YdDvK23+B9T+Sa8e+65p7zKLy6/8MIL4fHHHy/re9JJJ4V169b1VPcLL7ywfM19PN5rr71WPndn7dq15S0Ln332Wbj//vvDTz/9VB473opz6KGH9nQeOw1XYNB9J96Wvnr16rI/Ll26NMRfQw8//PDyeZXxeZHPPvtsePPNN8tKx7c3xl/DDzjggOEiOFtPAu+++26Iz4+spvg8pWqK6zdv3lwtlp8333xz23LdBWNPXam84obRf4w/efWJOqW9/vrrw9atW8vQODbccsst4fPPP59x1/j4kvjdp5fJ2NOL2mjvM6z+Y+wZ7X7QS+nWr19f/g21YsWKEB+hc8IJJ4R4i+/u3btD/Dtoy5Yt4b333isPHcedJ554IsTnoadOxp1UMfEEWgT6nlJ0QAIjLFAk5RoHH3xw85en4p9C23zxBXjGK7Hq/CIVq17cNtG44oor2o7bep599tln2isMR5hN0QqBQfad1r7V2lc654ukYOOVV17RHhkJxKv8Otux2/J0VWvtH9NdnVztY+ypJMbncxj9p7V/deubxp98+lW3dpxu27HHHjtt5Vr7hrFnWqKxXDldH+m2rtf+09q/uh3f2JNPN4t9oVtbVtuKN/k2ih8ppq1Ya78w7kxLZCWBOQmkp9yLf7kmArkKXHXVVaG45SDEK7pef/318jl98Reo+CbelStXhjVr1oT9999/TtVbuHBheeznnnuuvLrnk08+Cbt27QqLFi0Kxa2k5TnOPvvsOZ3DzsMXGGTfKRLG4cknnwwffPBB+Pjjj8OOHTvCH3/8EYrRPRx22GHhtNNOC5dffnmIV4cVCezhV94ZsxAw9mTRTCNXSOPPyDVJdgUy9mTXZCNRYGPPSDRDXwtRPAopxOegv/322+Grr75qfp9dsGBB+XfQkiVLwrJly8KqVav8vdVXeQcjUF9gXkwf1g8XSYAAAQIECBAgQIAAAQIECBAgQIBATgLeApxTaykrAQIECBAgQIAAAQIECBAgQIAAgUQBCcBEMOEECBAgQIAAAQIECBAgQIAAAQIEchKQAMyptZSVAAECBAgQIECAAAECBAgQIECAQKKABGAimHACBAgQIECAAAECBAgQIECAAAECOQlIAObUWspKgAABAgQIECBAgAABAgQIECBAIFFAAjARTDgBAgQIECBAgAABAgQIECBAgACBnAQkAHNqLWUlQIAAAQIECBAgQIAAAQIECBAgkCggAZgIJpwAAQIECBAgQIAAAQIECBAgQIBATgISgDm1lrISIECAAAECBAgQIECAAAECBAgQSBSQAEwEE06AAAECBAgQIECAAAECBAgQIEAgJwEJwJxaS1kJECBAgAABAgQIECBAgAABAgQIJApIACaCCSdAgAABAgQIECBAgAABAgQIECCQk4AEYE6tpawECBAgQIAAAQIECBAgQIAAAQIEEgUkABPBhBMgQIAAAQIECBAgQIAAAQIECBDISUACMKfWUlYCBAgQIECAAAECBAgQIECAAAECiQISgIlgwgkQIECAAAECBAgQIECAAAECBAjkJCABmFNrKSsBAgQIECBAgAABAgQIECBAgACBRAEJwEQw4QQIECBAgAABAgQIECBAgAABAgRyEpAAzKm1lJUAAQIECBAgQIAAAQIECBAgQIBAooAEYCKYcAIECBAgQIAAAQIECBAgQIAAAQI5CUgA5tRaykqAAAECBAgQIECAAAECBAgQIEAgUUACMBFMOAECBAgQIECAAAECBAgQIECAAIGcBCQAc2otZSVAgAABAgQIECBAgAABAgQIECCQKCABmAgmnAABAgQIECBAgAABAgQIECBAgEBOAhKAObWWshIgQIAAAQIECBAgQIAAAQIECBBIFJAATAQTToAAAQIECBAgQIAAAQIECBAgQCAnAQnAnFpLWQkQIECAAAECBAgQIECAAAECBAgkCkgAJoIJJ0CAAAECBAgQIECAAAECBAgQIJCTgARgTq2lrAQIECBAgAABAgQIECBAgAABAgQSBSQAE8GEEyBAgAABAgQIECBAgAABAgQIEMhJQAIwp9ZSVgIECBAgQIAAAQIECBAgQIAAAQKJAhKAiWDCCRAgQIAAAQIECBAgQIAAAQIECOQkIAGYU2spKwECBAgQIECAAAECBAgQIECAAIFEAQnARDDhBAgQIECAAAECBAgQIECAAAECBHISkADMqbWUlQABAgQIECBAgAABAgQIECBAgECigARgIphwAgQIECBAgAABAgQIECBAgAABAjkJSADm1FrKSoAAAQIECBAgQIAAAQIECBAgQCBRQAIwEUw4AQIECBAgQIAAAQIECBAgQIAAgZwEJABzai1lJUCAAAECBAgQIECAAAECBAgQIJAoIAGYCCacAAECBAgQIECAAAECBAgQIECAQE4CEoA5tZayEiBAgAABAgQIECBAgAABAgQIEEgUkABMBBNOgAABAgQIECBAgAABAgQIECBAICcBCcCcWktZCRAgQIAAAQIECBAgQIAAAQIECCQKSAAmggknQIAAAQIECBAgQIAAAQIECBAgkJOABGBOraWsBAgQIECAAAECBAgQIECAAAECBBIFJAATwYQTIECAAAECBAgQIECAAAECBAgQyElAAjCn1lJWAgQIECBAgAABAgQIECBAgAABAokCEoCJYMIJECBAgAABAgQIECBAgAABAgQI5CQgAZhTaykrAQIECBAgQIAAAQIECBAgQIAAgUQBCcBEMOEECBAgQIAAAQIECBAgQIAAAQIEchKQAMyptZSVAAECBAgQIECAAAECBAgQIECAQKKABGAimHACBAgQIECAAAECBAgQIECAAAECOQlIAObUWspKgAABAgQIECBAgAABAgQIECBAIFFAAjARTDgBAgQIECBAgAABAgQIECBAgACBnAQkAHNqLWUlQIAAAQIECBAgQIAAAQIECBAgkCggAZgIJpwAAQIECBAgQIAAAQIECBAgQIBATgISgDm1lrISIECAAAECBAgQIECAAAECBAgQSBSQAEwEE06AAAECBAgQIECAAAECBAgQIEAgJwEJwJxaS1kJECBAgAABAgQIECBAgAABAgQIJApIACaCCSdAgAABAgQIECBAgAABAgQIECCQk4AEYE6tpawECBAgQIAAAQIECBAgQIAAAQIEEgUkABPBhBMgQIAAAQIECBAgQIAAAQIECBDISUACMKfWUlYCBAgQIECAAAECBAgQIECAAAECiQL/A9HfRxfRx0j6AAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# choose number N of elements in partial sum \n",
    "N = 5\n",
    "\n",
    "# evaluate function and partial sum\n",
    "x = np.linspace(0,np.pi,200)\n",
    "Fe = f(x)\n",
    "Fs = computeSeries(x, N)\n",
    "\n",
    "# plot function and partial sum\n",
    "fig = plt.figure()\n",
    "plt.plot(x, Fe, color=\"blue\")\n",
    "plt.plot(x, Fs, color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Animating partial sums\n",
    "We now use an animation to see how the partial sums behave as $N$ increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "/* global mpl */\n",
       "window.mpl = {};\n",
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       "        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",
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       "        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",
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       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
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       "    this.rubberband_context = undefined;\n",
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       "    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",
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       "    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",
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       "        on_keyboard_event_closure('key_press')\n",
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       "    );\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",
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       "        'canvas'\n",
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       "            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",
       "    resizeObserver.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",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* 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\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.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",
       "\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"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.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",
       "\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.one(\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",
       "\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",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Nmax = 30\n",
    "\n",
    "output = []\n",
    "for n in range(1, Nmax+1):\n",
    "    output.append(computeSeries(x, n))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax  = plt.axes(xlim=(0,np.pi), ylim=(-0.2,3.0))\n",
    "line1, = ax.plot([], [], color = \"red\")\n",
    "line2, = ax.plot([], [], color = \"blue\")\n",
    "\n",
    "def update(t, line1, line2):\n",
    "    line1.set_data(x, Fe)\n",
    "    line2.set_data(x, output[t])\n",
    "    return [line1,line2]\n",
    "\n",
    "ani = animation.FuncAnimation(fig, update, frames=range(1, Nmax+1),\n",
    "                              fargs=[line1, line2], interval=200, blit=True, repeat=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}