docs: def plot_resp_slow(signal, rect_signal, sample_rate):
      """
      -----
      Brief
      -----
      Figure intended to represent the acquired RIP signal together with a rectangular signal defining
      inhalation and exhalation periods.
  
      -----------
      Description
      -----------
      Function design to generate a Bokeh figure containing the evolution of RIP signal, when
      slow respiration cycles occur, and the rectangular signal that defines the stages of
      inhalation and exhalation.
  
      Applied in the Notebook "Particularities of Inductive Respiration (RIP) Sensor ".
  
      ----------
      Parameters
      ----------
      signal : list
          List with the acquired RIP signal.
  
      rect_signal : list
          Data samples of the rectangular signal that identifies inhalation and exhalation
          segments.
  
      sample_rate : int
          Sampling rate of acquisition.
      """
  
      signal = numpy.array(signal) - numpy.average(signal)
      rect_signal = numpy.array(rect_signal)
      time = numpy.linspace(0, len(signal) / sample_rate, len(signal))
  
      # Inhalation and Exhalation time segments.
      # [Signal Binarisation]
      rect_signal_rev = rect_signal - numpy.average(rect_signal)
      inhal_segments = numpy.where(rect_signal_rev >= 0)[0]
      exhal_segments = numpy.where(rect_signal_rev < 0)[0]
      rect_signal_rev[inhal_segments] = numpy.max(rect_signal_rev)
      rect_signal_rev[exhal_segments] = numpy.min(rect_signal_rev)
  
      # [Signal Differentiation]
      diff_rect_signal = numpy.diff(rect_signal_rev)
      inhal_begin = numpy.where(diff_rect_signal > 0)[0]
      inhal_end = numpy.where(diff_rect_signal < 0)[0]
      exhal_begin = inhal_end
      exhal_end = inhal_begin[1:]
  
      # Generation of a Bokeh figure where data will be plotted.
      plot_aux = plot(list([0]), list([0]), showPlot=False)[0]
  
      # Edition of Bokeh figure (title, axes labels...)
      # [title]
      title = Title()
      title.text = "RIP Signal with slow cycles"
      plot_aux.title = title
  
      # [plot]
      plot_aux.line(time, signal, **opensignals_kwargs("line"))
      inhal_color = opensignals_color_pallet()
      exhal_color = opensignals_color_pallet()
      for inhal_exhal in range(0, len(inhal_begin)):
          if inhal_exhal == 0:
              legend = ["Inhalation", "Exhalation"]
          else:
              legend = [None, None]
  
          plot_aux.line(time[inhal_begin[inhal_exhal]:inhal_end[inhal_exhal]],
                    rect_signal_rev[inhal_begin[inhal_exhal]:inhal_end[inhal_exhal]],
                    line_width=2, line_color=inhal_color, legend=legend[0])
  
          if inhal_exhal != len(inhal_begin) - 1:
              plot_aux.line(time[exhal_begin[inhal_exhal]:exhal_end[inhal_exhal]],
                        rect_signal_rev[exhal_begin[inhal_exhal]:exhal_end[inhal_exhal]],
                        line_width=2, line_color=exhal_color, legend=legend[1])
          else:
              plot_aux.line(time[exhal_begin[inhal_exhal]:], rect_signal_rev[exhal_begin[inhal_exhal]:],
                        line_width=2, line_color=exhal_color, legend=legend[1])
  
      # [axes labels]
      plot_aux.xaxis.axis_label = "Time (s)"
      plot_aux.yaxis.axis_label = "Raw Data (without DC component)"
  
      show(plot_aux)