-----
    Brief
    -----
    Graphical representation of the EMG median power frequency evolution time series.

    -----------
    Description
    -----------
    Function intended to generate a Bokeh figure with 2x1 format, where each muscular activation
    period is identified through a colored box and the plot that shows the median frequency
    evolution is also presented.

    In the first cell is presented the EMG signal, highlighting each muscular activation.
    The second cell has the same time scale as the first one (the two plots are synchronized), being
    plotted the evolution time series of EMG median frequency.

    Per muscular activation period is extracted a Median Power Frequency value (sample), so, our
    window is a muscular activation period.

    Median power frequency is a commonly used parameter for evaluating muscular fatigue.
    It is widely accepted that this parameter decreases as fatigue sets in.

    Applied in the Notebook "Fatigue Evaluation - Evolution of Median Power Frequency".

    ----------
    Parameters
    ----------
    time_signal : list
        List with the time axis samples of EMG signal.

    signal : list
        List with EMG signal to present.

    time_median_freq : list
        List with the time axis samples of the median frequency evolution time-series.

    median_freq : list
        List with the Median Frequency samples.

    activations_begin : list
        List with the samples where each muscular activation period starts.

    activations_end : list
        List with the samples where each muscular activation period ends.

    sample_rate : int
        Sampling rate of acquisition.

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

def plot_median_freq_evol(time_signal, signal, time_median_freq, median_freq, activations_begin,
                          activations_end, sample_rate, file_name=None):
    """
    -----
    Brief
    -----
    Graphical representation of the EMG median power frequency evolution time series.

    -----------
    Description
    -----------
    Function intended to generate a Bokeh figure with 2x1 format, where each muscular activation
    period is identified through a colored box and the plot that shows the median frequency
    evolution is also presented.

    In the first cell is presented the EMG signal, highlighting each muscular activation.
    The second cell has the same time scale as the first one (the two plots are synchronized), being
    plotted the evolution time series of EMG median frequency.

    Per muscular activation period is extracted a Median Power Frequency value (sample), so, our
    window is a muscular activation period.

    Median power frequency is a commonly used parameter for evaluating muscular fatigue.
    It is widely accepted that this parameter decreases as fatigue sets in.

    Applied in the Notebook "Fatigue Evaluation - Evolution of Median Power Frequency".

    ----------
    Parameters
    ----------
    time_signal : list
        List with the time axis samples of EMG signal.

    signal : list
        List with EMG signal to present.

    time_median_freq : list
        List with the time axis samples of the median frequency evolution time-series.

    median_freq : list
        List with the Median Frequency samples.

    activations_begin : list
        List with the samples where each muscular activation period starts.

    activations_end : list
        List with the samples where each muscular activation period ends.

    sample_rate : int
        Sampling rate of acquisition.

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

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

    list_figures_1 = plot([list(time_signal), list(time_median_freq)],
                          [list(signal), list(median_freq)],
                          title=["EMG Acquisition highlighting muscular activations",
                                 "Median Frequency Evolution"], grid_plot=True,
                          grid_lines=2, grid_columns=1, open_signals_style=True,
                          x_axis_label="Time (s)",
                          yAxisLabel=["Raw Data", "Median Frequency (Hz)"],
                          x_range=[0, 125], get_fig_list=True, show_plot=False)

    # Highlighting of each processing window
    for activation in range(0, len(activations_begin)):
        color = opensignals_color_pallet()
        box_annotation = BoxAnnotation(left=activations_begin[activation] / sample_rate,
                                       right=activations_end[activation] / sample_rate,
                                       fill_color=color, fill_alpha=0.1)
        box_annotation_copy = BoxAnnotation(left=activations_begin[activation] / sample_rate,
                                            right=activations_end[activation] / sample_rate,
                                            fill_color=color, fill_alpha=0.1)
        list_figures_1[0].add_layout(box_annotation)
        list_figures_1[1].add_layout(box_annotation_copy)

    gridplot_1 = gridplot([[list_figures_1[0]], [list_figures_1[1]]],
                          **opensignals_kwargs("gridplot"))
    show(gridplot_1)