lab_reconstruct.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpolation and reconstruction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not obvious how to represent a continuous-time signal $x(t)$ in a way that can be manipulated by a computer.  The time index is real-valued, so even if we only care about the signal value over a finite interval there are infinitely many function values to consider and store.  \n",
    "\n",
    "One approach is to assume that $x(t)$ varies slowly, and hope that sampling $x(t)$ at $t=nT$ for $n$ integer and $T$ small is sufficient to characterise the signal.  This leads to the discrete-time signal $x[n] = x(nT)$.  When this assumption is formalised and holds then the method works, and we use it a lot.  The first part of this workbook explores how a discrete signal $x[n]$ can be used to represent or parameterise a continuous-time $x(t)$.  Understanding this link and its limitations lets us process \"analog\" signals $x(t)$ using digital processing of the corresponding $x[n]$.  See https://en.wikipedia.org/wiki/Digital_signal_processing.\n",
    "\n",
    "A more general option is to represent the continuous-time signal $x(t)$ as a linear combination of a fixed set of known basis functions $b_n(t)$:\n",
    "$$x(t) = \\sum_{n=0}^{N-1} c_n b_n(t).$$\n",
    "Here the signal $x(t)$ is defined for all $t \\in \\mathbb{R}$, but as written it is completely specified by the finite and discrete set of values $c_n, n=0, \\ldots, N-1$.  Not every signal $x(t)$ can be written in this way, and those that can depends on the choice of functions $b_n(t)$.\n",
    "\n",
    "One simple example uses a polynomial basis with $b_n(t) = t^n$.  The signals parameters $c_n$ are then just the coefficients for each polynomial order.  Another is the cosine basis $b_n(t) = \\cos(n \\omega_0 t)$, which can represent all periodic even functions $x(t)$ if $N$ is large enough.  Instead of summing from $0$ to $N-1$ in the representation we might also have infinite bounds like $0$ to $\\infty$, or $-\\infty$ to $\\infty$.  Even then, the coefficients $c_n$ are still a countable set and can be carefully used to represent or process the continuous-time $x(t)$ that they represent.  The second part of this workbook explores this concept."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reconstruction from discrete samples\n",
    "\n",
    "Suppose that $b_0(t)$ is an even function centered on the origin $t=0$, and for each $n$ the basis function $b_n(t)$ in the representation equation above is $b_0(t)$ shifted until it is centered on some point $t=nT$:  \n",
    "$$x(t) = \\sum_{n=0}^{N-1} c_n b_0(t-nT).$$\n",
    "This representation is particularly useful if we add the requirement\n",
    "$$b_0(t) = \\begin{cases}\n",
    "  1 \\qquad & t=0 \\\\\n",
    "  0 \\qquad & t=nT \\quad \\text{for integer $n$}.\n",
    "  \\end{cases}\n",
    "$$\n",
    "\n",
    "Suppose now that we have access to a discrete set of regular samples $x[n] = x(nT)$ from $x(t)$, and want to determine the corresponding coefficients $c_n$.  Note that\n",
    "$$x(kT) = \\sum_{n=0}^{N-1} c_n b_0(kT-nT) = \\sum_{n=0}^{N-1} c_n b_0((k-n)T) = c_k.$$\n",
    "The last step above follows because $b_0((k-n)T)=1$ only if $k-n=0$, so all the terms in the sum are zero except one.  You could also note that $b_0((k-n)T) = \\delta(k-n)$ and use the sifting property.  Either way $x[k] = x(kT) = c_k$, and the expansion takes the form\n",
    "$$x(t) = \\sum_{n=0}^{N-1} x[n] b_0(t-nT).$$\n",
    "\n",
    "We can view this as a reconstruction formula.  It takes as input the sample values in the form of a discrete signal $x[n]$ for integer $n$, and produces the interpolated or reconstructed continuous-time signal $x(t)$.  The nature of the reconstruction depends on the prototype basis function $b_0(t)$, and different choices lead to interpolation with different properties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic code\n",
    "\n",
    "We start off with an instance of a continuous-time signal and sample it according to $x[n] = x(nT)$ for some $T$.  The first subplot below shows a continuous-time signal $x(t)$, along with dots indicating a set of discrete samples of the signal.  The second subplot shows the corresponding discrete signal $x[n]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "\n",
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       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
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       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
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       "    );\n",
       "\n",
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       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
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       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
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       "\n",
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       "\n",
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       "    var size = msg['size'];\n",
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       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
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       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
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       "\n",
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       "    // Updates the figure title.\n",
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       "\n",
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       "    var cursor = msg['cursor'];\n",
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       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
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       "        break;\n",
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       "        break;\n",
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       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
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       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
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       "\n",
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       "\n",
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       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nicolls/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:26: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import sympy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "# Set up symbolic signal to approximate\n",
    "t = sp.symbols('t');\n",
    "#x = sp.cos((t-5)/5) - ((t-5)/5)**3;\n",
    "x = sp.exp(sp.cos(t)+t/10)\n",
    "lam_x = sp.lambdify(t, x, modules=['numpy']);\n",
    "\n",
    "# Discrete samples of signal over required range\n",
    "T = 2;  tmax = 20;\n",
    "nm = np.floor(tmax/T);\n",
    "tnv = T*np.arange(0,nm+1);\n",
    "xnv = lam_x(tnv);\n",
    "\n",
    "# Dense set of points for plotting \"continuous\" signals\n",
    "tv = np.linspace(-2, tmax+2, 2500);\n",
    "xv = lam_x(tv);\n",
    "    \n",
    "# Plots\n",
    "fh, ax = plt.subplots(2);\n",
    "ax[0].plot(tv,xv,'r-');  ax[0].scatter(tnv, xnv, c='r');  \n",
    "ax[0].set_ylabel('$x(t)$ and samples');  ax[0].set_xlabel('$t$');\n",
    "ax[1].stem(tnv, xnv);  ax[1].set_ylabel('$x[n]$');  ax[1].set_xlabel('$n$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below defines and plots a triangular reconstruction or interpolation kernel $b_0(t)$ that satisfies the required conditions, namely that $b_0(nT) = \\delta(n)$ for integer $n$.  Following that is the entire set of basis functions shown over the interval of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Interpolation kernel\n",
    "#b0 = sp.Piecewise( (0, t/T<=-1/2), (1, t/T<1/2), (0, True));  # rectangular\n",
    "#b0 = sp.Piecewise( (0, t/T<=-1), (1+t/T, t<0), (1-t/T, t/T<1), (0, True));  # triangular\n",
    "#at = sp.Abs(t/T);  a = -0.5;  b0 = sp.Piecewise( ((a+2)*at**3 - (a+3)*at**2 + 1 , at<1), (a*at**3 - 5*a*at**2 + 8*a*at - 4*a, at<2), (0, True));  # cubic\n",
    "at = sp.Abs(t/T);  w = 3;  b0 = sp.Piecewise( (sp.sin(sp.pi*at)/(sp.pi*at), at<=w), (0, True) );  # windowed sinc\n",
    "lam_b0 = sp.lambdify(t, b0.simplify(), modules=['numpy']);\n",
    "\n",
    "# Plot kernel and reconstruction basis\n",
    "tbv = np.linspace(-4*T, 4*T, 1500);\n",
    "b0v = lam_b0(tbv);\n",
    "fh, ax = plt.subplots(2);\n",
    "ax[0].plot(tbv, b0v, 'r-');\n",
    "for tn in tnv:\n",
    "    ax[1].plot(tv, lam_b0(tv-tn));\n",
    "ax[1].set_xlabel('t');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first subplot below shows a continuous-time signal $x(t)$, the dots indicating a set of discrete samples of the signal, and the signal reconstructed from the dots using the reconstruction kernel above.  The second subplot shows the separate scaled components that are summed to form the reconstruction.  We observe that the reconstruction corresponds to first-order linear interpolation between the sample points, so $b_0(t)$ in this case is the linear interpolation reconstruction kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Numerically evaluate linear interpolation approximation\n",
    "xav = np.zeros(tv.shape);\n",
    "for i in range(0,len(xnv)):   \n",
    "    xav = xav + xnv[i]*lam_b0(tv - tnv[i]);\n",
    "    \n",
    "# Plots\n",
    "fh, ax = plt.subplots(2);\n",
    "ax[0].plot(tv,xv,'r-',tv,xav,'b-');\n",
    "ax[0].scatter(tnv, xnv, c='r');\n",
    "for i in range(0,len(xnv)):   \n",
    "    ax[1].plot(tv,xnv[i]*lam_b0(tv - tnv[i]));\n",
    "ax[1].scatter(tnv, xnv, c='r');  ax[1].set_xlabel('t');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reconstruction above is quite good, but the condition that $b_0(nT) = \\delta(n)$ for integer $n$ is not sufficient for it to be a good reconstruction kernel.  The code below generates a reconstruction using another basis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bad interpolation\n",
    "b0a = sp.Piecewise( (0, t<=-T), (1+t/T, t<0), (1-t/T, t<T), (0, True))**5;\n",
    "lam_b0a = sp.lambdify(t, b0a, modules=['numpy']);\n",
    "\n",
    "# Numerically evaluate function and approximation\n",
    "xava = np.zeros(tv.shape);\n",
    "for i in range(0,len(xnv)):   \n",
    "    xava = xava + xnv[i]*lam_b0a(tv - tnv[i]);\n",
    "    \n",
    "# Plot\n",
    "fh, ax = plt.subplots(2);\n",
    "ax[0].plot(tv,xv,'r-',tv,xava,'b-');\n",
    "ax[0].scatter(tnv, xnv, c='r');\n",
    "for i in range(0,len(xnv)):   \n",
    "    ax[1].plot(tv,xnv[i]*lam_b0a(tv - tnv[i]));\n",
    "ax[1].scatter(tnv, xnv, c='r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This kernel again leads to perfect reconstruction of the sampled points, but the approximation is bad between them.  Evidently we also require that the interpolation kernel $b_0(t)$ also be smooth in some sense.  The ideal bandlimited interpolation kernel with no high-frequency components is the *sinc* function\n",
    "$$b_0(t) = \\frac{\\sin(\\pi t/T)}{\\pi t/T},$$\n",
    "which leads to the reconstruction below.  In this case if the signal $x(t)$ were actually bandlimited then the reconstruction would be exact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sinc interpolation\n",
    "b0b = sp.sin(sp.pi*t/T)/(sp.pi*t/T);\n",
    "lam_b0b = sp.lambdify(t, b0b, modules=['numpy']);\n",
    "\n",
    "# Numerically evaluate function and approximation\n",
    "xavb = np.zeros(tv.shape);\n",
    "for i in range(0,len(xnv)):   \n",
    "    xavb = xavb + xnv[i]*lam_b0b(tv - tnv[i]);\n",
    "    \n",
    "# Plot\n",
    "fh, ax = plt.subplots(2);\n",
    "ax[0].plot(tv,xv,'r-',tv,xavb,'b-');\n",
    "ax[0].scatter(tnv, xnv, c='r');\n",
    "for i in range(0,len(xnv)):   \n",
    "    ax[1].plot(tv,xnv[i]*lam_b0b(tv - tnv[i]));\n",
    "ax[1].scatter(tnv, xnv, c='r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One usually has some difficulty making the approximation accurate near the boundary of the observation interval.  Dealing with the boundary of a signal that is only observed for a finite time is a difficult part of signal processing:  the models usually assume time invariance, which means that functions are observed and go on forever."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation\n",
    "\n",
    "The representation formula used above suggests that we can reconstruct $x(t)$ using the expression\n",
    "$$x(t)= \\sum_{n=-\\infty}^{\\infty} x[n] b_0(t-nT).$$\n",
    "Here we've increased the range of the summation to include all $n$.  This can also be written as the convolution\n",
    "$$x_r(t) = x_s(t) \\ast b_0(t)$$\n",
    "where $x_s(t)$ is the modulated impulse train\n",
    "$$x_s(t) = \\sum_{n=-\\infty}^{\\infty} x[n] \\delta(t-nT).$$\n",
    "\n",
    "The interpretation is this:  given a discrete signal $x[n]$ obtained from regular samples of $x(t)$, we can generate a reconstruction $x_r(t)$ by forming a modulated impulse train $x_s(t)$ and filtering it with the reconstruction filter $b_0(t)$.  \n",
    "\n",
    "![](figures/sampxtime.png)\n",
    "\n",
    "If $x(t)$ is bandlimited with a sufficiently small sample spacing, and $b_0(t)$ is the ideal sinc interpolation kernel, then we will have $x_r(t) = x(t)$ and the representation or reconstruction is perfect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Radial basis function approximation\n",
    "\n",
    "Another interesting example that is investigated in this section is the use of *radial basis functions* (RBFs).  \n",
    "\n",
    "Suppose that $b_0(t)$ is again an even function centered on the origin $t=0$, and for each $n$ the basis function $b_n(t)$ is just $b_0(t)$ shifted until it is centered on some point $t=t_n$.  Then \n",
    "$$x(t) = \\sum_{n=0}^{N-1} c_n \\, b_0(t - t_n).$$\n",
    "The function $b_0(t)$ and the centers $t_0, \\ldots, t_{N-1}$ are specified in advance, and the coefficients $c_n$ are chosen so that the approximation matches the actual $x(t)$.\n",
    "\n",
    "For demonstration we use the prototype basis $b_0(t) = e^{-t^2/\\sigma^2}$ with $\\sigma=1$.  Earlier in this workbook we assumed $t_n=nT$, and while we do so again this is not in general required for an RBF network.  We also do not assume that $b_0(nT) = \\delta(n)$, and this makes it more difficult to specify the coefficients.  The prototype basis $b_0(t)$ and example basis functions $b_n(t) = b_0(t - t_n)$ in the representation are evaluated and plotted by the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 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) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\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",
       "\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",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    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.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import sympy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "# Prototype basis function\n",
    "sigma = 1;\n",
    "t = sp.symbols('t');\n",
    "b0r = sp.exp(-t**2/sigma**2);\n",
    "lam_b0r = sp.lambdify(t, b0r, modules=['numpy']);\n",
    "\n",
    "# Discretisation details and basis centers\n",
    "tmax = 12;\n",
    "Tc = 1;  nmc = int(np.floor(tmax/Tc));\n",
    "tnv = Tc*np.arange(0,nmc+1);\n",
    "#tnv = np.random.uniform(0, tmax, nm+1);  # random basis centers\n",
    "\n",
    "# Plot prototype and reconstruction basis\n",
    "tv = np.linspace(-2, tmax+2, 2500);\n",
    "tbv = np.linspace(-3*sigma, 3*sigma, 1500);  b0v = lam_b0r(tbv);\n",
    "fh, ax = plt.subplots(2);\n",
    "ax[0].plot(tbv, b0v, 'r-');\n",
    "for tn in tnv:\n",
    "    ax[1].plot(tv, lam_b0r(tv-tn));\n",
    "ax[1].set_xlabel('t');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the required values for $c_n$ given a function $x(t)$ to be approximated depends on the particular problem.  In this case we again simply require that the function $x(t)$ and the approximation agree on their values at $t=kT$ for some set of integers $k$.  As long as this set has more than $N$ elements then we have more than $N$ equations in $N$ unknowns, which can be solved if the resulting system is not underdetermined.  \n",
    "\n",
    "The code below calculates the coefficients for the function\n",
    "$$x(t) = \\cos\\left( \\frac{t-5}{5} \\right) - \\left( \\frac{t-5}{5} \\right)^3$$\n",
    "by formulating a matrix equation and solving.  Each value of $k$ leads to a single sample point and an equation of the form\n",
    "$$x(kT) = \\sum_{n=0}^{N-1} c_n b_0(kT - t_n)\n",
    "= \\begin{pmatrix} b_0(kT-t_0) & \\cdots & b_0(kT-t_{N-1}) \\end{pmatrix}\n",
    "\\begin{pmatrix} c_0 \\\\ \\vdots \\\\ c_{N-1} \\end{pmatrix}.$$\n",
    "If we consider $k=0, \\ldots, M-1$ this leads to the system of equations\n",
    "$$\\begin{pmatrix} x(0T) \\\\ \\vdots \\\\ x((M-1)T) \\end{pmatrix}\n",
    "= \\begin{pmatrix} \n",
    "b_0(0T-t_0) & \\cdots & b_0(0T-t_{N-1}) \\\\\n",
    "\\vdots & \\ddots & \\vdots \\\\\n",
    "b_0((M-1)T-t_0) & \\cdots & b_0((M-1)T-t_{N-1})\n",
    "\\end{pmatrix}\n",
    "\\begin{pmatrix} c_0 \\\\ \\vdots \\\\ c_{N-1} \\end{pmatrix},$$\n",
    "which is of the form $\\mathbf{b} = \\mathbf{B} \\mathbf{c}$.  In general if this system is overdetermined (which is usually true if $M>N$) then the least squares solution is often appropriate:\n",
    "$$\\mathbf{c} = (\\mathbf{B}^T \\mathbf{B})^{-1} \\mathbf{B}^T \\mathbf{b}.$$\n",
    "See https://en.wikipedia.org/wiki/Least_squares for details if interested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.35834594  0.46235455  0.64432842  0.5208424   0.56564884  0.56715441\n",
      "  0.55934324  0.50895771  0.34921072  0.18851101 -0.36759327 -0.33474544\n",
      " -2.44417749]\n"
     ]
    }
   ],
   "source": [
    "from numpy.linalg import pinv\n",
    "\n",
    "# Set up signal to approximate\n",
    "x = sp.cos((t-5)/5) - ((t-5)/5)**3;\n",
    "#x = sp.sin(t+0.5) + 5*(1 - sp.exp(-(t+0.5)));  # reconstruction points\n",
    "lam_x = sp.lambdify(t, x, modules=['numpy']);\n",
    "\n",
    "# Evaluate basis matrix and value for periodic observations\n",
    "M = int(1.0*len(tnv));  # M = 2*len(tnv);\n",
    "T = tmax/(M-1);\n",
    "B = np.zeros((M,len(tnv)));  b = np.zeros(M);\n",
    "for k in range(0,M):\n",
    "    b[k] = lam_x(k*T);\n",
    "    for n in range(0,len(tnv)):\n",
    "        B[k,n] = lam_b0r(k*T - tnv[n]);\n",
    "        \n",
    "# Solve for coefficients\n",
    "cv = pinv(B).dot(b);\n",
    "print(cv);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficient vector `cv` now specifies the particular instance of the approximation function.  The code below plots both the approximation and the actual function for a dense set of time points, and then shows the scaled basis contributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 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) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\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",
       "\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",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    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.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Numerically evaluate approximation (same as before)\n",
    "xav = np.zeros(tv.shape);\n",
    "for n in range(0,len(tnv)):   \n",
    "    xav = xav + cv[n]*lam_b0r(tv - tnv[n]);\n",
    "    \n",
    "# Plot\n",
    "fh, ax = plt.subplots(2);\n",
    "ax[0].plot(tv,lam_x(tv),'k-',tv,xav,'r-');\n",
    "ax[0].scatter(np.arange(0,M)*T, b, c='r');\n",
    "for n in range(0,len(tnv)):   \n",
    "    ax[1].plot(tv,cv[n]*lam_b0r(tv - tnv[n]));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the case where $M=N$ the series approximation will match the actual function values at all the points used to estimate the coefficients.  However, we have no control of the quality of the approximation between these points.  Also observe that if we increase $\\sigma$ then the approximation becomes smoother, and vice versa.  Without knowing the degree of smoothness of $x(t)$ it is however difficult to know what value is appropriate, and in general we might not even have an expression for it that we can plot.\n",
    "\n",
    "The radial basis function approach shown here is a local representation:  each basis function only influences on a small neighbourhood of points around its center.  This is convenient but isn't required.  A polynomial basis, for example, does not have this property, and neither does a sinusoidal basis.  \n",
    "\n",
    "Finding the coefficients for a general basis, even for the sampling-based method presented above which is not without flaws, is computationally complex:  for a $N$ element basis we have to invert a $N \\times N$ matrix.  With a different basis we can improve on this situation.  For example, the requirement that $b_0(nT) = \\delta(n)$ used earlier in this workbook imposes an *orthogonality* condition on the problem that makes it easier to find the coefficients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tasks\n",
    "\n",
    "These tasks involve writing code, or modifying existing code, to meet the objectives described.\n",
    "\n",
    "1.  Suppose we have samples $x[n] = x(nT)$ of the signal\n",
    "$$x(t) = \\cos\\left( \\frac{t-5}{5} \\right) - \\left( \\frac{t-5}{5} \\right)^3$$\n",
    "for $n=0, \\ldots, N-1$, with $N=10$ and $T = 1.2$.  Consider the reconstruction equation\n",
    "$$x_r(t) = \\sum_{n=0}^{N-1} x[n] b_0(t-nT).$$\n",
    "On the same set of axes plot both $x(t)$ and $x_r(t)$ over the range $t = 0$ to $t=(N-1)T$ for the case of a box filter interpolant $b_0(t) = p_T(t) = p_1(t/T)$, where $p_T(t)$ is the unit pulse of total width $T$ centered on the origin.<br><br>\n",
    "\n",
    "2.  Generate two new plots with the same specifications as for the previous task, but using these interpolation kernels:\n",
    "\n",
    "    A.  Cubic kernel $b_0(tT) = \n",
    "    \\begin{cases}\n",
    "      (a+2)|t|^3 - (a+3)|t|^2 + 1 \\qquad & 0 \\leq |t| < 1 \\\\\n",
    "      a|t|^3 - 5a|t|^2 + 8a|t| - 4a \\qquad & 1 \\leq |t| < 2 \\\\\n",
    "      0 \\qquad & 2 \\leq |t|\n",
    "    \\end{cases}\n",
    "    $ <br>\n",
    "    with $a=-1/2$.\n",
    "\n",
    "    B.  Truncated or windowed sinc function $b_0(tT) = \\frac{\\sin(\\pi t)}{\\pi t} p_{2w}(t)$ for $w=3$.<br><br>\n",
    "    \n",
    "3.  Use a representation of the form\n",
    "$$x_r(t) = \\sum_{n=0}^{N-1} c_n b_n(t)$$\n",
    "with a polynomial basis $b_n(t) = t^n$ to approximate the signal $x(t) = e^{(t/10 + cos(t))}$.  The coefficients should be calculated using least squares from the samples $x[n] = x(nT)$ for $T=1$ and $n=0, \\ldots, M-1$.  On a single set of axes show the signal $x(t)$, the reconstruction $x_r(t)$, and the sampled points, for the case of $N=9$ and $M=11$.  The domain of the plot should be from $t=0$ to $t=10$.<br><br>\n",
    "\n",
    "4.  (optional) Repeat the previous task using a cosine basis $b_n(t) = \\cos(\\omega_0 n t)$ with $\\omega_0 = 2\\pi/10$ and $N = 6$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hints and guidance\n",
    "\n",
    "The remainder of this notebook provides a more detailed walkthrough of possible ways of addressing the given tasks.  You may not need them.\n",
    "\n",
    "## Task 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.A) Set up symbolic signal x\n",
    "# Create a symbolic variable 't', and specify the given x in terms of t. Use 'lambdify' to create a function 'lam_x'\n",
    "# that takes t as an input and gives x(t) out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.B) Get the continuous x(t)\n",
    "# Create an array tv of 2500 time points over the range you're interested in. Get xv, the continuous approximation of the signal\n",
    "# x, by applying the lambda function you just created to each value in tv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.C) Get the discrete x[n]\n",
    "# Create the an array 'tnv' that contains the values of nT for n = 0 to n = N. Sample x by applying the lam_x function to each \n",
    "# value in tnv. Call the resulting array xnv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.D) Get b0\n",
    "# Use 'piecewise' to create the rectangular pulse b0. (Hint: the line of code you'll need should be in the 'Interpolation\n",
    "# kernel' cell.) Create a lambda function lam_b0 that takes t as the input and returns b0(t).\n",
    "\n",
    "# OPTIONAL: create an array 'b0v' by applying lam_b0 to a time array (tv or some new array, tbv, over a preferable range.)\n",
    "# plot b0v to see what each individual basis function looks like. You can also use a for loop to plot all the basis functions\n",
    "# over the range of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.E) Reconstruct\n",
    "# Create an array 'xav' to hold the reconstructed signal. It should be the same size as tv. Use a for loop to add each scaled\n",
    "# basis function to the reconstruction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.F) Output\n",
    "# Plot the original signal xv and the reconstructed signal xav on the same set of axes vs tv. If you like, you can use subplot\n",
    "# to create a second set of axes and plot each basis function on it using a for loop to better visualize what's happening."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.A.A) Get b0\n",
    "# You've already set up the signal and created continuous and discrete versions of it in task 1.\n",
    "# Use 'piecewise' to create the cubic interpolation function b0 and lambdify it. (Again, Prof's done it for you. You just have to\n",
    "# uncomment it and change the parameters.)\n",
    "\n",
    "# OPTIONAL: plot b0 as described in 1.D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.A.B) Reconstruct and output\n",
    "# Create the reconstructed signal xav and plot it on the same set of axes as the original signal, as described in 1.E and 1.F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2.B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.B.A) Get b0\n",
    "# Use 'piecewise' to create the sinc interpolation function b0 and lambdify it.\n",
    "\n",
    "# OPTIONAL: plot b0 as described in 1.D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.B.B) Reconstruct and output\n",
    "# Create the reconstructed signal xav and plot it on the same set of axes as the original signal, as described in 1.E and 1.F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3\n",
    "\n",
    "The idea of reconstruction from basis functions and the way this task is formulated probably seem unfamiliar to you, but if you expand out that sum, you get $x(t) = c_0 b_0 + c_1 b_1 + ... + c_N b_N $\n",
    "\n",
    "Substituting in $b_n = t^n$, you get $x(t) = c_0 + c_1 t + c_2 t^2 ... + c_N t^N $ so all we're actually trying to do here is come up with an Nth order polynomial we can use to approximate $x(t)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.A) Set up symbolic x\n",
    "# Specify the signal x in terms of the symbolic variable t and lambdify it to create lam_x\n",
    "# Create the continuous approximation x(t) as described in 1.B\n",
    "\n",
    "t = sp.symbols('t');\n",
    "x = sp.exp(sp.cos(t)+t/10)\n",
    "lam_x = sp.lambdify(t, x, modules=['numpy']);t = sp\n",
    "\n",
    "tv = np.linspace(0,10,2500);\n",
    "xv = lam_x(tv);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.B Finding coefficients with least squares\n",
    "We're going to use samples of $x(t)$ at $M$ points to evaluate the coefficients.\n",
    "\n",
    "At each individual sample, $x(kT) = \\sum_{n=0}^{N-1} c_n b_n(kT) = c_0 + c_1 (kT) + c_2 (kT)^2 + ... c_N (kT)^N$, which can be written as the matrix expression: $$x(kT)\n",
    "= \\begin{pmatrix} 1 & kT & (kT)^2 & \\cdots & (kT)^N) \\end{pmatrix}\n",
    "\\begin{pmatrix} c_0 \\\\ c_1 \\\\ c_2\\\\ \\vdots \\\\ c_N \\end{pmatrix}.$$\n",
    "\n",
    "By repeating this for k values from 0 through M, we get $$\\begin{pmatrix} x(0T) \\\\ x(1T) \\\\ \\vdots \\\\ x(MT) \\end{pmatrix}\n",
    "= \\begin{pmatrix} 1 & 0T & \\cdots & (0T)^N\n",
    "\\\\ 1 & 1T & \\cdots & (1T)^N\n",
    "\\\\ \\vdots\n",
    "\\\\ 1 & MT & \\cdots & (MT)^N\n",
    "\\end{pmatrix}\n",
    "\\begin{pmatrix} c_0 \\\\ c_1 \\\\ \\vdots \\\\ c_N \\end{pmatrix}$$ or $b = Bc$\n",
    "\n",
    "We can calculate each term in $b$ using the lam_x function. The matrix $B$ is also simple to evaluate. From there, we just have to invert $B$ and multiply it by $b$ to get the coefficient matrix $c$ out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.B)\n",
    "# Use 'zeros' to create the M x 1 matrix 'b' and the M x N matrix 'B'. Populate these matrices with the values described above\n",
    "# using for loops. Solve for the coefficient matrix cv by taking the dot product of the inverse of B and b.\n",
    "# (Hint: you can get an idea of how to do each of these tasks by looking at the least squares code.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.C) Reconstruct and output\n",
    "# Create an array xav, the same size as tv, to use for your polynomial approximation. Use a for loop to build the polynomial by\n",
    "# adding each c_n*t**n term from 0 up to N.\n",
    "# plot the original signal and the polynomial on the same set of axes."
   ]
  }
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