MRSP_FiltersAndWindows.ipynb

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        "<center>\n",
        "    <img src=\"https://github.com/TUIlmenauAMS/MRSP_Tutorials/blob/master/images/msp_header.png?raw=1\">\n",
        "</center>\n",
        "\n",
        "#### Prof. Dr. -Ing. Gerald Schuller <br> Jupyter Notebook: Renato Profeta"
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      "source": [
        "# Filters and Windows"
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        "Last time we saw how we can obtain the impulse response of the ideal low pass filter, the sinc function. The problem of this impulse response is, that it is non-causal, and we also cannot make it causal because it starts at minus infinity (we cannot add infinite delay).\n",
        "\n",
        "<img src=\"https://github.com/TUIlmenauAMS/MRSP_Tutorials/blob/master/images/msp_05_01_sinc_function.png?raw=1\" width=500>\n",
        "\n",
        "Assume we would like to have a causal Finite Impulse Response (**FIR**) low pass filter, meaning the impulse response starts a time 0 and extents over only a finite amount of samples, n=0,…,L-1, for a length L. (Observe that FIR filters have no correspondence in the analog domain, analog filters always have an infinite impulse response, **IIR**)\n",
        "\n",
        "How do we design this filter such that it becomes “similar” in some sense to the ideal? (Since we cannot get the ideal filter)"
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        "%%html\n",
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        "**Example:** Design an anti-aliasing filter for sampling rate conversion: The original signal is from a CD with 44.1 kHz sampling rate, hence an audio bandwidth of about up to  around 20 kHz, and we would like to downsample it to 22 kHz sampling rate, for example for a Mac computer. In order do be able to downsample it, we first have to low pass filter it, for instance the pass-band should be from 0 to 9 kHz, and the stop-band, to avoid aliasing of frequencies above 11 kHz, should be from 11 kHz on to the maximum (here: 22.05 kHz). Since we don't have an ideal filter, we need to include a “transition” band, here from 9kHz to 11 kHz, to let the filter “transition” from pass band to stop band, with intermediate attenuations.\n",
        "\n",
        "Aliasing is something that is easily perceivable by the ear, hence we would like to have **at least 60 dB** attenuation from 11 kHz and up. The passband can take some ripples in the frequency response, for instance ± 2 dB (corresponding to about ±25% in voltage). The frequency range from 9 kHz to 11 kHz is the so-called transition band, which gives the filter space to build up its attenuation from 0 to 60 dB, to make sure it already has this 60 dB at 11 kHz frequency. In this way we can formulate requirements for a “similarity” of our filter, even though we know we cannot reach the optimum (a practical approach).\n",
        " \n",
        "A first (perhaps naïve) approach is to define the similarity as the quadratic error of the frequency response of our FIR filter to our given ideal frequency response (mostly this means the magnitude of the frequency response). This has the advantage of being mathematically very simple. This means our goal is to **minimize** this **quadratic error**. Assume our desired frequency response is $H_d(\\omega)$, and the (real)  frequency response of our causal FIR filter is $H(\\omega)$. Then the quadratic error $e$ is\n",
        "\n",
        "$$ \\large\n",
        " {e= \\int _{ {0} } ^ { {2π} }  { \\mid H_ {{d} }  ( ω )  - { H ( ω )}  \\mid  ^ { {2} } dω} } {}\n",
        "$$\n"
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        "%%html\n",
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        "We would like to make this error *e* as small as possible.\n",
        "Observe that we need an integral here because the frequency domain signal is continuous, since the time domain signal is not periodic (only then would the Fourier transform be discrete over the frequency). But here we have the sinc function with infinite extent over time. Observe that because of this we also cannot apply the Discrete Fourier Transform, because it is made for periodic signals with a finite period (the DFT is usually applied just to this finite period). But the Discrete Time Fourier Transform is for discrete time signals with infinite period.\n",
        "\n",
        "We need to minimize the quadratic error *e* to find the best approximation with our FIR filter. We cannot solve this problem in the frequency domain, but we can solve the equivalent formulation in the time domain.\n",
        "\n",
        "To obtain this, we use the so-called **Parseval Theorem**, which states that the power of a signal (the sum of its magnitude squared) is the same in the time- and in the frequency-domain (this is true for the Discrete Time Fourier Transform as well as for the DFT).\n",
        "\n",
        "$$ \\large\n",
        " {e= \\int_{ {0} }  ^{ {2π} }  { \\mid H _ { {d} }  ( ω )  - H ( ω )\\mid ^ { {2} } dω= \\sum _{ {n= -  \\infty } } ^ { { \\infty } }  { \\mid h _ { {d} }  ( n )  - h ( n )  \\mid  ^ { {2} } } } } {}\n",
        "$$\n",
        "\n",
        "The beauty here is now, that we obtain a sum, which we now can compute more easily (remember: We want to obtain h(n)).\n",
        "\n",
        "For our ideal low pass filter, $h_d(n)$ was the sinc function. To obtain a causal filter, we already allowed a time shift $n_d$ for the ideal impulse response (using our FIR filter h(n) makes causality possible). Plugging this into the above equation yields:\n",
        "\n",
        "$$ \\large\n",
        "{e= \\sum _{ {n= -  \\infty } }  ^ { { \\infty } }  { \\mid \\frac    {sin (  ( n - n _ { {d} }  ) \\omega_{{p} }  ) }   { ( n - n _ { {d} }  ) \\omega_ { {p} } } }  - h ( n )  \\mid  ^ { {2} } } {}\n",
        "$$\n",
        "\n",
        "How should we choose h(n) and delay $n_d$ to obtain the minimum possible quadratic error e for a given length L of h(n)?\n",
        "\n",
        "Given **the length L**, we need to determine the **L coefficients h(n)** and the **delay parameter $n_d$**.\n",
        "\n",
        "To make the solution more easy to see, we can **divide** the sum into 2 parts, one over the extend of h(n) and one for the rest:\n",
        "\n",
        "$$ \n",
        "{e= \\sum _{ {n= 0 } }  ^ { { L - 1} }  { \\mid \\frac    {sin (  ( n - n _ { {d} }  ) \\omega_{{p} }  ) }   { ( n - n _ { {d} }  ) \\omega_ { {p} } } }  - h ( n )  \\mid  ^ { {2} } } {}\n",
        "+{ \\sum _{ {n<0 } }  ^ { { n>L-1 } }  { \\mid \\frac    {sin (  ( n - n _ { {d} }  ) \\omega_{{p} }  ) }   { ( n - n _ { {d} }  ) \\omega_ { {p} } } }   \\mid  ^ { {2} } } {}\n",
        "$$\n",
        "\n",
        "Both terms are positive, so to minimize e we have to minimize both terms. To minimize the right hand term, we only have $n_d$. We can choose $n_d$ such only the smallest values appear there, and that the biggest values of $h_d$ are not “seen” by the right hand sum, and covered by h(n) in the left hand sum. We can imagine the left hand sum being a so-called “rectangular window”, where we can shift the sinc function over this window function to obtain h(n). \n",
        "\n",
        "The rectangular window is a function which has the value 1 inside the window length and the value 0 outside the window length. The finite sum can be imagined as resulting from first multiplying the infinite sinc function with this rectangular window, and then compute the sum, which hence becomes finite.\n",
        "\n",
        "The goal here would be to shift the sinc function, using the delay $n_d$, such that the window for h(n) “sees” the **biggest values** of the sinc  function. Observe that this leads to a contiguous impulse response. Using the above formulas, it would also be possible to just pick the **biggest magnitude** values of the  sinc function for h(n) (the maxima and minima), for a non-contiguous impulse response, which would lead to a smaller quadratic error for L coefficients! (Question: How would the resulting frequency response look like?).\n",
        "\n",
        "Assume L=2, then we have a filter with 2 coefficients. The biggest 2 values in the sinc function are around  the center, around zero, hence we would like to choose $n_d=0.5$ (a delay of half a sample), so that the new center is around n=0.5, and the right hand side sum “sees” only the smaller values. In general we would like to choose $n_d=(L-1)/2$, meaning we shift the **maximum** of the sinc function exactly in the **center** of our window for h(n).\n",
        "\n",
        "What do we do with the left hand side (inside our window)? We choose h(n) identical to $h_d(n)$, and the left hand sum (with the window) becomes indeed zero!\n",
        "\n",
        "This way we get a simple “recipe” for designing an FIR filter with minimum squared error: Take the center of the sinc function (or the ideal impulse response $h_d$), and window it with a so-called “**rectangular” window** (because it has a rectangular form in time or space. Inside the sum for h(n) this imagined window has a value of 1, and outside it has a value of 0).\n",
        "\n",
        "![sinc_window](https://github.com/TUIlmenauAMS/MRSP_Tutorials/blob/master/images/msp_05_02_sinc_window.JPG?raw=1)\n",
        "\n",
        "This imagined window function will become more interesting if we modify the values from 1 to other values.\n",
        "We can see: FIR filter = Sinc function **times** the rectangular window.\n",
        "\n",
        "**Observe:** If we only use a finite piece of the Sinc function as our filter, we **implicitly** already applied a **rectangular window**. Hence there is no need to apply it explicitly any more.\n",
        "\n",
        "This **multiplication** of the rectangular window with the ideal impulse response in the **time domain** becomes a **convolution** of the DTFT of the rectangular window with the ideal frequency response in the **frequency domain**. In this way we can see the result in the frequency domain. In effect, the ideal  frequency response (of the sinc function) is “blurred” by convolving it with the DTFT of the window function. Ideally, this DTFT of the window should be an impulse at frequency zero, because then the convolution would not change the ideal frequency response.  But this would mean an infinitely long window in the time domain. We have a finite rectangular window in the time domain which becomes another **Sinc function** in the **frequency domain**, quite different from a pulse at frequency zero!\n",
        "\n",
        "**Oberve:** The longer the window in the time domain, the more narrow its sinc function in the frequency domain becomes, and the more similar to an impulse. Hence, for better filters we need a longer window!"
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      "source": [
        "Lets see what we really have, in iPython, with window:"
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "L=11\n",
        "h = np.concatenate((np.zeros(4),np.ones(L),np.zeros(4)),axis=0)\n",
        "plt.figure()\n",
        "plt.stem(np.arange(-4, L+4, step=1), h, use_line_collection=True)\n",
        "plt.xticks(np.arange(-4, L+4, step=1))\n",
        "plt.grid();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dOMV09cPSrFr",
        "outputId": "8ad3d5dd-420e-4ce2-902f-da72f46a5d33",
        "colab": {
          "referenced_widgets": [
            "77423bd7deb1434b8a8da5b6322bb4ed"
          ]
        }
      },
      "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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=\"900\">"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "77423bd7deb1434b8a8da5b6322bb4ed",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=16, description='L', max=128, min=2), Checkbox(value=False, description=…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from ipywidgets import interact\n",
        "import ipywidgets as widgets\n",
        "from matplotlib.ticker import FuncFormatter, MultipleLocator\n",
        "from scipy.signal import argrelextrema\n",
        "import scipy.signal as signal\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9,4))\n",
        "fig.suptitle('Rectangular Window Frequency Response', fontsize=16)\n",
        "plt.subplots_adjust(wspace=0.3)\n",
        "\n",
        "@interact(L=widgets.IntSlider(min=2, max=128, step=1, value=16),unwrap=False)\n",
        "def plot_recWindow_H(L=16, unwrap=False):\n",
        "    h = np.concatenate((np.ones(L),np.zeros(4)),axis=0)\n",
        "    omega, H =signal.freqz(h[:-4],worN=512)\n",
        "    H[np.abs(H) < np.finfo(float).eps] = np.finfo(float).eps\n",
        "    H_dB=20*np.log10(np.abs(H)/np.abs(H).max())\n",
        "    p3dB=np.max(H_dB)-3\n",
        "    p3dBIndex= np.max(np.where(H_dB >= p3dB))\n",
        "    wp=omega[p3dBIndex]\n",
        "    Hp=H_dB[p3dBIndex]\n",
        "    ax1.clear()\n",
        "    ax1.plot(omega, H_dB)\n",
        "    ax1.plot(wp, Hp,'go')\n",
        "    ax1.plot((0, wp), (Hp, Hp), 'k--') \n",
        "    ax1.plot((wp, wp), (-80, Hp), 'k--') \n",
        "    ax1.set_ylim((-80,H_dB.max()+5))\n",
        "    ax1.annotate(r'$\\omega$={:.2f}$\\pi$,-3dB'.format(wp/np.pi), xy=(wp, Hp), xytext=(wp+0.2*np.pi, 0),\n",
        "            arrowprops=dict(facecolor='black', shrink=0.02, width=1, headwidth=4),)\n",
        "    if L!=2:\n",
        "        side_lobe=argrelextrema(np.abs(H), np.greater)[0][0]\n",
        "        ax1.plot((0, omega[-1]),(H_dB[side_lobe],H_dB[side_lobe]), 'k--') \n",
        "        ax1.annotate(r'$\\omega$={:.2f}$\\pi$, {:.2f}dB'.format(omega[side_lobe]/np.pi,H_dB[side_lobe]), \n",
        "                     xy=(omega[side_lobe], H_dB[side_lobe]), xytext=(omega[side_lobe]+0.2*np.pi, -5),\n",
        "                    arrowprops=dict(facecolor='black', shrink=0.02, width=1, headwidth=4),)\n",
        "        ax1.plot(omega[side_lobe], H_dB[side_lobe],'go')\n",
        "        \n",
        "    ax1.xaxis.set_major_formatter(FuncFormatter(lambda val,pos: '{:.0g}$\\pi$'.format(val/np.pi) if val !=0 else '0'))\n",
        "    ax1.xaxis.set_major_locator(MultipleLocator(base=0.2*np.pi))\n",
        "    ax1.grid(True)\n",
        "    ax1.set_xlabel(r'$\\omega$ [rad/sample]')\n",
        "    ax1.set_ylabel(r'Normalized Magnitude [dB]')\n",
        "    \n",
        "    \n",
        "    ax2.clear()\n",
        "    phase=np.angle(H,deg=True)\n",
        "    if unwrap:\n",
        "        phase=np.unwrap(phase)\n",
        "    ax2.plot(omega,phase)\n",
        "    ax2.xaxis.set_major_formatter(FuncFormatter(lambda val,pos: '{:.0g}$\\pi$'.format(val/np.pi) if val !=0 else '0'))\n",
        "    ax2.xaxis.set_major_locator(MultipleLocator(base=0.2*np.pi))\n",
        "    ax2.grid(True)\n",
        "    ax2.set_xlabel(r'$\\omega$ [rad/sample]')\n",
        "    ax2.set_ylabel(r'Phase [degrees]')\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yA3rNQyiSrFr"
      },
      "source": [
        "Here we can see that it is far from being an impulse at frequency zero. In fact it is somewhat broad and also the attenuation is not very high, on the order of -15 to -20 dB. We expect that our resulting low pass filter will “inherit” these properties through the convolution in the frequency domain.\n",
        "\n",
        "The “pass band” or main lobe width of our window function will determine the **transition band width** of our resulting filter, and the stop band attenuation of the window will determine the **resulting stop band attenuation** of our filter.\n",
        "\n",
        "This shows that the **window function shapes the key characteristics** of our resulting FIR filter!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQOeglTKSrFr"
      },
      "source": [
        "**Example**: Take our example of a downsampling filter which should attenuate frequencies starting at 11 kHz, at a sampling rate of 44.1 kHz. We would like to have -60 dB attenuation in the stopband. Hence we obtain the normalized frequency for the start of our stop band as:\n",
        "$$\\large\n",
        "\\omega_s=\\frac{11 }{ 44.1 }\\cdot 2 \\cdot \\pi \\approx 0.5 \\cdot \\pi\n",
        "$$\n",
        "\n",
        "Hence our desired frequency response is 1 between frequency 0 and $\\omega_s$ (or better: between $-\\omega _s$ to $\\omega _s$ to also include the negative frequency axis). Now take the inverse DTFT of the ideal, desired frequency response $H_d(\\omega)$ to obtain the ideal impulse response $h_d(n)$. Since at first we assume an ideal filter, we set the end of the pass band $\\omega_c$ identical to the beginning of the stop band, $\\omega_c=\\omega_s$:\n",
        "\n",
        "$$\\large\n",
        "h_d(n)= \\frac{1} {2 \\pi} \\cdot \\int _{\\omega=-\\pi} ^ \\pi H_d(\\omega) e^{j \\omega n} d \\omega= $$\n",
        "\n",
        "$$\\large\n",
        "=\\frac {1}  {2 \\pi} \\cdot \\int _{\\omega=-\\omega_c} ^ {\\omega_c}  e^{j \\omega n} d \\omega =\n",
        "$$\n",
        "\n",
        "$$\\large\n",
        "= \\frac{1}  {2 \\pi} \\left(\\frac {e^{j \\cdot \\omega_c n}} {j \\cdot n}- \\frac{e^{-j \\cdot \\omega_c n}} {j \\cdot n}\\right)=  \\frac{sin(\\omega_c n)}  {\\pi n}\n",
        "$$\n",
        "\n",
        "(remember: $sin(\\omega)=\\frac{1} {2j} \\cdot (e^{j \\omega} -e^{-j \\omega})$ )\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OeMo3fHwSrFr"
      },
      "source": [
        "## Approximation of an Ideal Low Pass Filter using a Rectangular Window and Delay"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gs2PyMPzSrFs"
      },
      "source": [
        "We've seen that the impulse response of an ideal low pass filter is:\n",
        "\n",
        "$$\\large\n",
        "h(n)=\\frac{sin(\\omega_c \\cdot n)}{\\pi \\cdot n }$$\n",
        "\n",
        "Truncating the impulse response (multiplying by a rectangular window) so it has a **length L** and applying a **delay of $n_d=\\frac{(L-1)}{2}$** we have:\n",
        "\n",
        "$$\\large\n",
        "h(n)=\\frac{sin\\left(\\omega_c \\cdot (n-n_d)\\right)}{\\pi \\cdot (n-d_d)} = \n",
        "\\frac{sin\\left(\\omega_c \\cdot \\left(n-\\frac{(L-1)}{2}\\right)\\right)}{\\pi \\cdot \\left(n-\\frac{(L-1)}{2}\\right)}\n",
        "$$\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "hide_input": true,
        "id": "JF8WPzJfSrFs",
        "outputId": "8cd8d8a1-3490-489e-cb80-d2ccaee31135"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<center>\n",
              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/AuuucABMpvg\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
              "</center>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "%%html\n",
        "<center>\n",
        "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/AuuucABMpvg\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
        "</center>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7JDIaccpSrFs",
        "outputId": "c0ff811b-fed3-4ea8-8ea7-4432b6efb487"
      },
      "outputs": [
        {
          "data": {
            "text/latex": [
              "$\\displaystyle \\frac{\\sin{\\left(\\omega_c \\left(- \\frac{L}{2} + n + \\frac{1}{2}\\right) \\right)}}{\\pi \\left(- \\frac{L}{2} + n + \\frac{1}{2}\\right)}$"
            ],
            "text/plain": [
              "sin(\\omega_c*(-L/2 + n + 1/2))/(pi*(-L/2 + n + 1/2))"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sympy import sin, symbols, simplify, pi, lambdify, limit\n",
        "\n",
        "wc_s = symbols('\\omega_c')\n",
        "n_s, L_s = symbols('n L', integer=True)\n",
        "\n",
        "n_d_s=(L_s-1)/2\n",
        "h_n_s=sin(wc_s*(n_s-n_d_s))/(pi*(n_s-n_d_s))\n",
        "h_n_s"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qMFDmZ9JSrFs",
        "outputId": "3a0c3256-103e-4650-988e-6191bac0fdf7"
      },
      "outputs": [
        {
          "data": {
            "text/latex": [
              "$\\displaystyle \\frac{\\omega_c}{\\pi}$"
            ],
            "text/plain": [
              "\\omega_c/pi"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "h_0_s = limit(h_n_s,n_s,n_d_s)\n",
        "h_0_s"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DC2Qkb_vSrFs",
        "outputId": "36deeacd-3052-4d8a-9bf5-ed7b2704bd78",
        "colab": {
          "referenced_widgets": [
            "e56f8108108c42988af66578da95c803"
          ]
        }
      },
      "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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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              "<img 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\" width=\"700\">"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e56f8108108c42988af66578da95c803",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=16, description='L', max=128, min=2), FloatSlider(value=0.5, description…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "h_n=lambdify((wc_s,n_s,L_s), h_n_s, 'numpy')\n",
        "\n",
        "fig1, (ax1, ax2,ax3) = plt.subplots(3, 1, figsize=(7,7))\n",
        "fig1.suptitle('FIR Rectangular Window', fontsize=16)\n",
        "plt.subplots_adjust(wspace=0.3)\n",
        "\n",
        "\n",
        "@interact(L=widgets.IntSlider(min=2, max=128, step=1, value=16), \n",
        "          wc=widgets.FloatSlider(min=0.1, max=1, step=0.01, value=0.5), unwrap=False)\n",
        "def plot_h(L=16, wc=0.5,unwrap=False):\n",
        "    wc=wc*np.pi\n",
        "    n=np.arange(L)\n",
        "    n_d=n_d_s.subs(L_s,L).evalf()\n",
        "    if L%2==0:\n",
        "        h=h_n(wc,n,L)\n",
        "    else:\n",
        "        h=np.ones(L)\n",
        "        for i in range(L):\n",
        "            if i==n_d:\n",
        "                h[i]=wc/np.pi\n",
        "            else:\n",
        "                h[i]=h_n(wc,i,L)\n",
        "    ax1.clear()\n",
        "    ax1.stem(h, use_line_collection=True)\n",
        "    ax1.plot(h,'r--',alpha=0.4)\n",
        "    ax1.set_title(r'Impulse Response')\n",
        "    ax1.grid()\n",
        "    \n",
        "    omega, H =signal.freqz(h,worN=2048)\n",
        "    H[np.abs(H) < np.finfo(float).eps] = np.finfo(float).eps\n",
        "    H_dB=20*np.log10(np.abs(H))\n",
        "    p3dB=-3\n",
        "    p3dBIndex_down= np.max(np.where(H_dB >= p3dB))\n",
        "    p3dBIndex_up= np.min(np.where(H_dB <= p3dB))\n",
        "    print(H_dB[p3dBIndex_down])\n",
        "    print(H_dB[p3dBIndex_up])\n",
        "    if (np.abs(p3dB-H_dB[p3dBIndex_down]) >= np.abs(p3dB-H_dB[p3dBIndex_up])):\n",
        "        p3dBIndex=p3dBIndex_up\n",
        "    else:\n",
        "        p3dBIndex=p3dBIndex_down\n",
        "    wp=omega[p3dBIndex]\n",
        "    Hp=H_dB[p3dBIndex]\n",
        "    max_peak_ind=np.argmax(H_dB)\n",
        "    \n",
        "    ax2.clear()\n",
        "    ax2.plot(omega, H_dB)\n",
        "    ax2.plot(omega[max_peak_ind], H_dB[max_peak_ind],'go')\n",
        "    ax2.plot((0, omega[max_peak_ind]), (H_dB[max_peak_ind], H_dB[max_peak_ind]), 'k--',alpha=0.5) \n",
        "    ax2.plot((omega[max_peak_ind], omega[max_peak_ind]), (-80, H_dB[max_peak_ind]), 'k--',alpha=0.5)\n",
        "    ax2.annotate('$\\omega_{{peak}}$={:.2f}$\\pi$,{:.2f} dB'.format(omega[max_peak_ind]/np.pi,H_dB[max_peak_ind]), \n",
        "                 xy=(omega[max_peak_ind],H_dB[max_peak_ind]), xytext=(omega[max_peak_ind]+0.15*np.pi, 0),\n",
        "            arrowprops=dict(facecolor='black', shrink=0.02, width=1, headwidth=4))\n",
        "    ax2.plot(wp, Hp,'go')\n",
        "    ax2.plot((0, wp), (Hp, Hp), 'k--', alpha=0.5) \n",
        "    ax2.plot((wp, wp), (-80, Hp), 'k--',alpha=0.5)\n",
        "    ax2.set_ylabel(r'Magnitude [dB]')\n",
        "    ax2.grid()\n",
        "    ax2.set_ylim((-60,H.max()+5))\n",
        "    ax2.xaxis.set_major_formatter(FuncFormatter(lambda val,pos: '{:.2f}$\\pi$'.format(val/np.pi) if val !=0 else '0'))\n",
        "    ax2.xaxis.set_major_locator(MultipleLocator(base=0.25*np.pi))\n",
        "    ax2.annotate(r'$\\omega_{{-3dB}}$={:.2f}$\\pi$,{:.2f} dB'.format(wp/np.pi,Hp), xy=(wp, Hp), xytext=(wp+0.15*np.pi, -10),\n",
        "            arrowprops=dict(facecolor='black', shrink=0.02, width=1, headwidth=4),)\n",
        "    if L!=2:\n",
        "        side_lobe=argrelextrema(np.abs(H[p3dBIndex:]), np.greater)[0][0]+p3dBIndex\n",
        "        ax2.plot((0, omega[-1]),(H_dB[side_lobe],H_dB[side_lobe]), 'k--',alpha=0.5) \n",
        "        ax2.annotate(r'$\\omega$={:.2f}$\\pi$, {:.2f}dB'.format(omega[side_lobe]/np.pi,H_dB[side_lobe]), \n",
        "                     xy=(omega[side_lobe], H_dB[side_lobe]), xytext=(0, -40),\n",
        "                    arrowprops=dict(facecolor='black', shrink=0.02, width=1, headwidth=4),)\n",
        "        ax2.plot(omega[side_lobe], H_dB[side_lobe],'go')\n",
        "        \n",
        "    ax3.clear()\n",
        "    phase=np.angle(H, deg=False)\n",
        "    if unwrap:\n",
        "        phase=np.unwrap(phase)\n",
        "        print('Delay={:.2f}'.format(phase[p3dBIndex]/wp))\n",
        "    ax3.plot(omega,phase)\n",
        "    ax3.xaxis.set_major_formatter(FuncFormatter(lambda val,pos: '{:.2f}$\\pi$'.format(val/np.pi) if val !=0 else '0'))\n",
        "    ax3.xaxis.set_major_locator(MultipleLocator(base=0.25*np.pi))\n",
        "    ax3.grid(True)\n",
        "    ax3.set_xlabel(r'$\\omega$ [rad/sample]')\n",
        "    ax3.set_ylabel(r'Phase')\n",
        "    ax3.plot(wp, phase[p3dBIndex],'go')\n",
        "    ax3.annotate(r'$\\omega$={:.2f}$\\pi$, {:.2f}'.format(wp/np.pi,phase[p3dBIndex]), \n",
        "                     xy=(wp, phase[p3dBIndex]), xytext=(0, -1),\n",
        "                    arrowprops=dict(facecolor='black', shrink=0.02, width=1, headwidth=4),)\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPC4tIXBSrFt"
      },
      "source": [
        "Now assume we would like to have a filter length of $L=16$. Then our delay to make the resulting filter causal is $n_d=\\dfrac{(L-1)}{2}=7.5$.\n",
        "Hence our filter becomes:\n",
        "\n",
        "$$\\large\n",
        "h(n)=\\frac{sin(\\omega_c(n-7.5))}{\\pi(n-7.5)} \\text{for n=0,...,15.}$$\n",
        "\n",
        "This is now our resulting causal FIR filter with a rectangular window function.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iyhy2U9TSrFt"
      },
      "source": [
        "The pass band has ripples of about +/- 0.5 dB. 0.5 dB corresponds to a factor of $10^{\\frac{0.5}{20}}\\approx 1.06$, which means only 6% over- or under-shoot, and is usually okay.\n",
        "\n",
        "But, here we can also see that this filter has maybe -10 dB attenuation at normalized frequency $0.5 \\cdot \\pi$, and the first side lobe has only about -20 dB attenuation. Hence it does not satisfy or requirement of at least -60 dB attenuation starting at normalized frequency $0.5 \\cdot \\pi$.\n",
        "\n",
        "For the phase plot: We have a delay of $n_d=7.5$ with our filter. Since this is a so-called linear phase filter, we expect the phase to be $-7.5\\cdot \\omega$. We can verify this in our plot, for instance at frequency $\\omega=0.1\\cdot \\pi$, in our plot at x-axis 0.1. In Degrees we obtain $7.5\\cdot 0.1\\cdot\\pi\\cdot \\frac{180}{\\pi}=135$. This is what we indeed roughly observe in the plot.\n",
        "\n",
        "\n",
        "We now have a filter with the **minimum squared error** in comparison with our given ideal. But is this really what we like to have?\n",
        "\n",
        "The problem that we get is called **“Gibbs Phenomenon”**. It says that the error appears as ripples along the magnitude of the pass band and the stop band. The interesting part is, that the **ripples** near the pass band and the stop band edge **don’t become smaller** as L becomes larger (smaller error in the approximation), but they **only become more narrow!** This is the result of **convolving** our ideal frequency response with the **sinc function** from the rectangular window. This sinc function only becomes narrower as we increase its length L, but the **height** of its **ripples stays** the same.\n",
        "\n",
        "This means, the **maximum error** that we obtain does not become smaller as we increase L! (which is bad)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-FtQS_mSrFt"
      },
      "source": [
        "**Observe:** The ripple size near the pass-band and stop-band edge only becomes more narrow (hence reduced area and hence reduced quadratic error), but their **height does not become smaller** with increasing L. Here they always stay at aroun 0.1 in the stop-band, which corresponds to only about -20 dB! (Which would not be sufficiente in our aliasing filter example.)\n",
        "\n",
        "To see how the Gibbs Phenomenon results, we can take a look at our scheme in the frequency domain: \n",
        "\n",
        "In principle we multiplied our ideal impulse response with the rectangular window. In the frequency domain, this means a **convolution of the ideal frequency response** (with a phase change through the delay  $𝑛_𝑑$ ) with the **frequency response of the rectangular window**. The latter is a narrow sinc function, with corresponding ripples to the sides of its main lobe. And those ripples are what shows up as Gibbs Phenomenon. If we make our window longer (L larger), the sinc function becomes more narrow, but the height of the ripples does not decrease, but stays constant!\n",
        "\n",
        "Actually in most applications, what we want is not minimizing the quadratic error, but **minimizing the maximum error**! Basically, we chose the **wrong error measure.**\n",
        "\n",
        "This also suggests a modification to lower the height of the ripples. Instead of a rectangular window, we can take **alternative windows**, which have lower ripples in the frequency domain.\n",
        "\n",
        "Commonly used windows are the so-called raised cosine or the sine windows."
      ]
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