def plot_low_pass_filter_response(show_plot=False, file_name=None):
    """
    -----
    Brief
    -----
    Taking into consideration the generic transfer function that defines the frequency response of
    a low-pass filter (|H|=1/(sqrt(1+(f/fc)^2n), where fc is the corner frequency and n is the
    filter order), the current function will generate a figure for comparing the frequency response
    accordingly to the filter order.

    -----------
    Description
    -----------
    In digital and analogical systems, a filter defines a system capable of attenuating specific
    frequency components of the signal that is applied to it.

    The filter behaviour can be mathematically defined through a transfer function, showing
    precisely the stop- and pass-bands.

    In the case of a low-pass filter, a structural parameter is the corner frequency (where the
    attenuation begins). Like the name suggests, all signal components with frequency below the
    corner frequency will be outputted without changes (they "pass" the filter), while components
    with frequency above the corner frequency suffers an attenuation (the bigger the difference
    between the frequency of the component and the corner frequency the bigger the attenuation).

    It is shown (in the same figure) the low-pass filter response for different order (1, 2, 3, 4,
    5, 6).

    Applied in the Notebook "Digital Filtering - A Fundamental Pre-Processing Step".

    ----------
    Parameters
    ----------
    show_plot : bool
        If True then the generated figure/plot will be shown to the user.

    file_name : str
        Path containing the destination folder where the Bokeh figure will be stored.

    Returns
    -------
    out : list
        List of Bokeh figures that compose the generated gridplot.
    """

    # Generation of the HTML file where the plot will be stored.
    #file_name = _generate_bokeh_file(file_name)

    # Frequency list.
    freqs = numpy.linspace(1, 1200, 100000)
    cutoff_freq = 40

    # Generation of filter response.
    gain_functions = []
    legend_strs = []
    for order in range(1, 7):
        gain = 20*numpy.log10(1 / (numpy.sqrt(1 + (freqs / cutoff_freq)**(2*order))))

        # Storage of the determined gain values.
        gain_functions.append(gain)
        if order == 1:
            legend_strs.append("1st order filter")
        elif order == 2:
            legend_strs.append("2nd order filter")
        elif order == 3:
            legend_strs.append("3rd order filter")
        else:
            legend_strs.append(str(order) + "th order filter")


    # Generation of a Bokeh figure with the opensignals style.
    fig_list = plot([freqs / cutoff_freq]*len(gain_functions), gain_functions, legend=legend_strs,
                    title="Filter Response", x_axis_label="Normalized Frequency",
                    y_axis_label="Gain (dB)", x_axis_type="log", x_range=(0.1, 40),
                    y_range=(-120, 5), show_plot=True, get_fig_list=True)

    # Inclusion of a colored region showing the ideal behaviour.
    color=opensignals_color_pallet()
    box_annotation = BoxAnnotation(left=0.1, right=1, top=0, bottom=-120,
                                   fill_color=color,
                                   fill_alpha=0.3)
    fig_list[0].circle([-100], [0], fill_color=color, fill_alpha=0.3, legend="Ideal Filter Response")
    fig_list[0].add_layout(box_annotation)

    # Show figure.
    if show_plot is True:
        show(fig_list[0])