fourier_series_approx_1.ipynb

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   "cell_type": "markdown",
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    "<hr style=\"height: 1px;\">\n",
    "<i>This code was authored by Alex Shvonski, Copyright 2020 MIT All Rights Reserved.</i>\n",
    "<hr style=\"height: 1px;\">\n",
    "\n",
    "<h2>Initializing the program</h2>\n",
    "\n",
    "<font size=\"3\">To initialize the visualization, you may need to click \"Run all initialization cells\" above (see button location in figure).</font>\n",
    "\n",
    "<img src=\"img/binder_initialize_button.png\" alt=\"Drawing\" style=\"width: 700px;\" align=\"left\"/>\n",
    "\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<br/>\n",
    "<font size=\"3\">You should see the visualization <i>directly below this line</i>, before the next section.</font>\n",
    "\n",
    "<br/>\n",
    "<br/>\n",
    "<hr style=\"height: 1px;\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [],
   "source": [
    "#This code was authored by Alex Shvonski, Copyright 2020 MIT All Rights Reserved.\n",
    "\n",
    "%matplotlib notebook\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display, clear_output\n",
    "from ipywidgets import interact, interactive, interactive_output, fixed, IntSlider, HBox, Layout, Output, VBox\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,\n",
    "                               AutoMinorLocator)\n",
    "from math import pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "#Define functions\n",
    "###############################################\n",
    "###############################################\n",
    "def func1(t):\n",
    "    result = (abs((t%1)-0.25) < 0.25).astype(float) - (abs((t%1)-0.75) < 0.25).astype(float)\n",
    "    return result\n",
    "\n",
    "def func2(t):\n",
    "    result = t % 1\n",
    "    return result\n",
    "\n",
    "def func3(t):\n",
    "    result = 4*((t%1)-0.5)**2\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "#Define Fourier coefficients\n",
    "###############################################\n",
    "###############################################\n",
    "def a0(func,t):\n",
    "    #this approximates integration\n",
    "    c = func(t)\n",
    "    return 2.*c.sum()/c.size\n",
    "\n",
    "def an(func,t,n):\n",
    "    #this approximates integration\n",
    "    T = 1.\n",
    "    c = func(t)*np.cos(2.*n*pi*t/T)\n",
    "    return 2.*c.sum()/c.size\n",
    "\n",
    "def bn(func,t,n):\n",
    "    #this approximates integration\n",
    "    T = 1.\n",
    "    c = func(t)*np.sin(2.*n*pi*t/T)\n",
    "    return 2.*c.sum()/c.size\n",
    "\n",
    "def fourier_series_2(func,t,Nh):\n",
    "    #Nh=Nh+1\n",
    "    T = 1.\n",
    "    f = np.zeros((Nh+1,len(t)))\n",
    "            \n",
    "    if Nh==0:\n",
    "        result = 0.5*a0(func,t)*np.ones(len(t))\n",
    "\n",
    "    if Nh>0:\n",
    "        f[0] = 0.5*a0(func,t)*np.ones(len(t))\n",
    "        for i in np.arange(1,Nh+1,1):    \n",
    "            f[i] = an(func,t,i)*np.cos(2.*(i)*pi*t/T) + bn(func,t,i)*np.sin(2.*(i)*pi*t/T)\n",
    "        \n",
    "        result = np.sum(f.real,axis=0)\n",
    "    \n",
    "    return result\n",
    "    \n",
    "\n",
    "def nth_cos_term(func,t,n):\n",
    "    T = 1.\n",
    "    f = an(func,t,n)*np.cos(2.*n*pi*t/T)\n",
    "    return f\n",
    "\n",
    "def nth_sin_term(func,t,n):\n",
    "    T = 1.\n",
    "    f = bn(func,t,n)*np.sin(2.*n*pi*t/T)\n",
    "    return f\n"
   ]
  },
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   "execution_count": 9,
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       "        }\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 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 = $(\"#\" + 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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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"
    },
    {
     "data": {
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width=\"950\">"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "570d3080572b438382f5c8b291239c69",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Dropdown(description='Function:', options=(('square wave', <function func1 at 0x11703bb70>), ('…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Define plot\n",
    "###############################################\n",
    "###############################################\n",
    "fig, ax = plt.subplots(1, 2, figsize=(9.5, 5))\n",
    "plt.subplots_adjust(left=0.05, bottom=None, right=1, top=None, wspace=None, hspace=1.)\n",
    "t = np.linspace(0., 2., 10000)\n",
    "func=func1\n",
    "Nh=5\n",
    "#############################\n",
    "#plot1\n",
    "y_max = max(func(t))*2.5\n",
    "\n",
    "line_1, = ax[0].plot(t, func(t),\n",
    "                     'k--', lw=2, label=\"Function\")\n",
    "\n",
    "line_2, = ax[0].plot(t, fourier_series_2(func,t,Nh),\n",
    "                    'c-', lw=3, label=\"Fourier Sum\")\n",
    "\n",
    "#plot params\n",
    "ax[0].set_title('Amplitude: $A(t)$', fontsize=16)\n",
    "ax[0].set_xlabel('$t$', fontsize=16)\n",
    "ax[0].set_ylim(-y_max,y_max)\n",
    "ax[0].set_xlim(min(t),max(t))\n",
    "ax[0].grid(True)\n",
    "ax[0].legend(loc=1, fontsize=16)\n",
    "ax[0].xaxis.set_major_locator(MultipleLocator(0.5))\n",
    "plt.setp(ax[0].get_xticklabels(), fontsize=14)\n",
    "plt.setp(ax[0].get_yticklabels(), fontsize=14)\n",
    "\n",
    "\n",
    "#############################\n",
    "#plot2\n",
    "#y_max = max(amplitude(w,w0,gamma,f0))*1.3\n",
    "\n",
    "#plot blue for odd, red for even (green zero)\n",
    "#if Nh == 0:\n",
    "#    line_3, = ax[1].plot(t, 0.5*a0(func,t)*np.ones(len(t)),\n",
    "#                'g-', lw=2, label=\"zeroth term\")\n",
    "\n",
    "\n",
    "#if Nh > 0:       \n",
    "line_3, = ax[1].plot(t, nth_cos_term(func,t,Nh),\n",
    "                     'b-.', lw=2, label=\"cos\")\n",
    "line_4, = ax[1].plot(t, nth_sin_term(func,t,Nh),\n",
    "                     'r-', lw=2, label=\"sin\")\n",
    "\n",
    "#plot params\n",
    "ax[1].set_title('nth Fourier Components', fontsize=16)\n",
    "ax[1].set_xlabel('$t$', fontsize=16)\n",
    "ax[1].set_ylim(-y_max,y_max)\n",
    "ax[1].set_xlim(min(t),max(t))\n",
    "ax[1].grid(True)\n",
    "ax[1].legend(loc=1, fontsize=16)\n",
    "ax[1].xaxis.set_major_locator(MultipleLocator(0.5))\n",
    "plt.setp(ax[1].get_xticklabels(), fontsize=14)\n",
    "plt.setp(ax[1].get_yticklabels(), fontsize=14)\n",
    "\n",
    "\n",
    "#Define plot updater\n",
    "###############################################\n",
    "###############################################\n",
    "def update(func,Nh):\n",
    "    #plot1\n",
    "    #############################\n",
    "    line_1.set_ydata(func(t))\n",
    "    \n",
    "    line_2.set_ydata(fourier_series_2(func,t,Nh))\n",
    "         \n",
    "    #plot2\n",
    "    #############################\n",
    "    if Nh == 0:\n",
    "        line_3.set_ydata(0.5*a0(func,t)*np.ones(len(t)))\n",
    "        line_4.set_ydata(np.zeros(len(t)))\n",
    "\n",
    "    if Nh > 0:       \n",
    "        line_3.set_ydata(nth_cos_term(func,t,Nh))\n",
    "        line_4.set_ydata(nth_sin_term(func,t,Nh))\n",
    "    \n",
    "    fig.canvas.draw_idle()\n",
    "    return\n",
    "\n",
    "\n",
    "#Define control elements\n",
    "###############################################\n",
    "###############################################\n",
    "s1=widgets.Dropdown(\n",
    "    options=[('square wave', func1), ('saw-tooth', func2), ('parabola', func3)],\n",
    "    value=func1,\n",
    "    description='Function:')\n",
    "\n",
    "s2=widgets.IntSlider(\n",
    "    min=0,\n",
    "    max=20,\n",
    "    step=1,\n",
    "    value=7,\n",
    "    layout=Layout(width='600px'),\n",
    "    description='Number of Fourier terms: $n=$',\n",
    "    style = {'description_width': 'initial'})\n",
    "\n",
    "\n",
    "#Connect controls to plot\n",
    "###############################################\n",
    "###############################################\n",
    "out = interactive_output(update, {'func': s1, 'Nh': s2})\n",
    "\n",
    "\n",
    "#Set layout\n",
    "###############################################\n",
    "###############################################\n",
    "box_layout = Layout(display='flex', flex_flow='row', justify_content='space-between', align_items='center')\n",
    "\n",
    "\n",
    "#Display output\n",
    "###############################################\n",
    "###############################################\n",
    "d = display(HBox([s1, s2], layout=box_layout))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height: 1px;\">\n",
    "\n",
    "<h2>About the Visualization</h2>\n",
    "\n",
    "<font size=\"3\">These plots show the Fourier series approximation of different functions. One can select a function, which is shown as a dashed line in Plot 1 (left), and generate a Fourier series of the function (shown as cyan line in Plot 1) by setting the number of terms to include in the series. In Plot 2 (right) the amplitude of each term is shown (for both $\\sin$ and $\\cos$ terms).\n",
    "</font>\n",
    "\n",
    "\n",
    "<br/>\n",
    "<font size=\"3\">\n",
    "<b>Plot 1 (left):</b>\n",
    "<ul>\n",
    "    <li>(dashed black line) the function that is chosen to be approximated.</li>\n",
    "    <li>(cyan line) the sum $n$ terms in the Fourier series approximation (where $n$ is chosen by the user).</li>\n",
    "</ul>\n",
    "    \n",
    "    \n",
    "<br/>\n",
    "<b>Plot 2 (right):</b>\n",
    "<ul>\n",
    "    <li>(blue dot-dashed line) the $n^\\mathrm{th}$ cosine term in the Fourier series</li>\n",
    "    <li>(red line) the $n^\\mathrm{th}$ sine term in the Fourier series</li>\n",
    "</ul>\n",
    "  \n",
    "  \n",
    "<br/>\n",
    "<b>Drop-down and Sliders:</b>\n",
    "<ul>\n",
    "    <li>Drop-down: select the function to be approximated</li>\n",
    "    <li>Slider: choose the number of terms to include in the Fourier series; the amplitude of the $n^\\mathrm{th}$ cosine and sine terms will be displayed in Plot 2.</li>\n",
    "</ul>\n",
    "</font>\n",
    "<br/>\n",
    "\n",
    "<hr style=\"height: 1px;\">\n",
    "\n",
    "<h2>Exploration</h2>\n",
    "\n",
    "<font size=\"3\">Consider the following questions and possible actions:\n",
    "<ul>\n",
    "    <li>How does the Fourier series approximation change when you include more terms?</li>\n",
    "    <li>Which functions have only cosine or sine terms?</li>\n",
    "    <li>Which functions have only odd or even terms?</li>\n",
    "</ul>      \n",
    "</font>\n",
    "\n",
    "<br/>\n",
    "\n",
    "<hr style=\"height: 1px;\">\n",
    "\n",
    "<h2>Viewing the Code</h2>\n",
    "<br/>\n",
    "<font size=\"3\">You are encouraged to click the button below to view the source code. You can alter the code and rerun it within this notebook, or download the notebook itself and run the code locally on your own machine.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "init_cell": true
   },
   "outputs": [
    {
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       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The code in this block includes content from StackOverFlow User: harshil (CC BY-SA 4.0)\n",
    "#and can be found here: https://stackoverflow.com/questions/27934885/how-to-hide-code-from-cells-in-ipython-notebook-visualized-with-nbviewer\n",
    "###############################################\n",
    "###############################################\n",
    "#Enable hidden code\n",
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')\n"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}