docs: def plot_before_after_filter(signal, sr, band_begin, band_end, order=1, x_lim=[], y_lim=[],
                               orientation="hor", show_plot=False, file_name=None):
      """
      -----
      Brief
      -----
      The use of the current function is very useful for comparing two power spectrum's (before and
      after filtering the signal).
      This function invokes "plot_informational_band" in order to get the power spectrum before
      applying the signal to the lowpass filter.
  
      -----------
      Description
      -----------
      The FFT Power Spectrum, of an input signal, can be generated through plotfft function of
      novainstrumentation package (or periogram function of scipy package).
      The x axis (freqs) represents the frequency components of the signal, after decomposition was
      achieved by applying the Fourier Transform. The y axis (power) defines the relative weight of
      each frequency component (sinusoidal function) in the process of reconstructing the signal by
      re-summing of decomposition components.
  
      It is presented a 1x2 gridplot for compaing the differences in frequency composition of the
      signal under analysis (before and after filtering).
  
      Additionally, it is also graphically presented a rectangular box showing which are the frequency
      components with relevant information for studying our input physiological signal.
  
      Applied in the Notebook "Digital Filtering - A Fundamental Pre-Processing Step".
  
      ----------
      Parameters
      ----------
      signal : list
          List containing the acquired signal samples.
  
      sr : int
          Sampling rate.
  
      band_begin : float
          Lower frequency inside the signal informational band.
  
      band_end : float
          Higher frequency inside the signal informational band.
  
      order : int
          Filter order.
  
      x_lim : list
          A list with length equal to 2, defining the first and last x value that should be presented.
  
      y_lim : list
          A list with length equal to 2, defining the first and last y value that should be presented.
  
      orientation : str
          If "hor" then the generated figures will be joined together in an horizontal gridplot.
          When "vert" the gridplot will be a vertical grid and when "same" the plots are generated at
          the same figure.
  
      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)
  
      # Generation of FFT power spectrum accordingly to the filter order.
      for i in range(0, order + 1):
          # Initialisation and appending of data to the figures list.
          if i == 0:
              # Power spectrum
              freqs_after, power_after = plotfft(signal, sr)
              figure_after = plot_informational_band(freqs_after, power_after, signal, sr,
                                                     band_begin, band_end,
                                                     legend="Signal Power Spectrum", x_lim=x_lim,
                                                     y_lim=y_lim)
              # List that store the figure handler
              list_figures = [[figure_after]]
          else:
              filter_signal = lowpass(signal, f=band_end, order=i, fs=sr)
  
              # Power spectrum
              freqs_after, power_after = plotfft(filter_signal, sr)
  
              if orientation != "same":
                  figure_after = plot_informational_band(freqs_after, power_after, filter_signal, sr,
                                                         band_begin, band_end,
                                                         legend="Filtered FFT (Order " + str(i) + ")",
                                                         x_lim=x_lim, y_lim=y_lim)
                  # Append data accordingly to the desired direction of representation.
                  if orientation == "hor":
                      # Append to the figure list the power spectrum of the signal after filtering.
                      list_figures[-1].append(figure_after)
                  elif orientation == "vert":
                      list_figures.append([figure_after])
              else:
                  list_figures[-1][0].line(freqs_after, power_after, legend="Filtered FFT (Order " + str(i) + ")",
                                           **opensignals_kwargs("line"))
  
      # Show gridplot.
      grid_plot_1 = gridplot(list_figures, **opensignals_kwargs("gridplot"))
  
      if show_plot is True:
          show(grid_plot_1)
  
      return list_figures