docs: def make_multi_spectrum_plots(model_plot, plotids, data_getter, desc, xmin_clamp=0.01,
                                min_valid_x=None, max_valid_x=None):
      """Make a plot of multiple spectral models and data.
  
      *model_plot*
        A model plot object returned by Sherpa from a call like
        ``ui.get_model_plot()`` or ``ui.get_bkg_model_plot()``.
      *data_plots*
        An iterable of data plot objects returned by Sherpa from calls like
        ``ui.get_source_plot(id)`` or ``ui.get_bkg_plot(id)``.
      *desc*
        Text describing the origin of the data; will be shown in the plot legend
        (with "Model" and "Data #<number>" appended).
      *xmin_clamp*
        The smallest "x" (energy axis) value that will be plotted; default is 0.01.
        This is needed to allow the plot to be shown on a logarithmic scale if
        the energy axes of the model go all the way to 0.
      *min_valid_x*
        Either None, or the smallest "x" (energy axis) value in which the model and
        data are valid; this could correspond to a range specified in the "notice"
        command during analysis. If specified, a gray band will be added to the plot
        showing the invalidated regions.
      *max_valid_x*
        Like *min_valid_x* but for the largest "x" (energy axis) value in which the
        model and data are valid.
      Returns:
        A tuple ``(plot, xlow, xhigh)``, where *plot* an OmegaPlot RectPlot
        instance, *xlow* is the left edge of the plot bounds, and *xhigh* is the
        right edge of the plot bounds.
  
      TODO: not happy about the code duplication with :func:`make_spectrum_plot`
      but here we are.
  
      """
      import omega as om
      from omega.stamps import DataThemedStamp, WithYErrorBars
  
      model_x = np.concatenate((model_plot.xlo, [model_plot.xhi[-1]]))
      model_x[0] = max(model_x[0], xmin_clamp)
      model_y = np.concatenate((model_plot.y, [0.]))
  
      # Sigh, sometimes Sherpa gives us bad values.
      is_bad = ~np.isfinite(model_y)
      if is_bad.sum():
          from .cli import warn
          warn('bad Sherpa model Y value(s) at: %r', np.where(is_bad)[0])
          model_y[is_bad] = 0
  
      p = om.RectPlot()
      data_csps = []
      data_lines = []
      xlow = xhigh = None
  
      for index, plotid in enumerate(plotids):
          data_plot = data_getter(plotid)
          data_left_edges = data_plot.x - 0.5 * data_plot.xerr
          data_left_edges[0] = max(data_left_edges[0], xmin_clamp)
          data_hist_x = np.concatenate((data_left_edges, [data_plot.x[-1] + 0.5 * data_plot.xerr[-1]]))
          data_hist_y = np.concatenate((data_plot.y, [0.]))
  
          if xlow is None:
              xlow = model_x[0]
              xhigh = model_x[-1]
          else:
              xlow = min(xlow, model_x[0])
              xhigh = max(xhigh, model_x[-1])
  
          csp = om.rect.ContinuousSteppedPainter(keyText=None)
          csp.setFloats(data_hist_x, data_hist_y)
          data_csps.append(csp)
  
          inner_stamp = DataThemedStamp(None)
          stamp = WithYErrorBars(inner_stamp)
  
          lines = om.rect.XYDataPainter(
              lines = False,
              pointStamp = stamp,
              keyText = '%s Data #%d' % (desc, index)
          )
          lines.setFloats(data_plot.x, data_plot.y,
                          data_plot.y + data_plot.yerr,
                          data_plot.y - data_plot.yerr)
          inner_stamp.setHolder(lines)
          data_lines.append(lines)
  
      log_bounds_pad_factor = 0.9
      xlow *= log_bounds_pad_factor
      xhigh /= log_bounds_pad_factor
  
      if min_valid_x is not None:
          p.add(om.rect.XBand(1e-3 * xlow, min_valid_x, keyText=None), zheight=-1, dsn=1)
      if max_valid_x is not None:
          p.add(om.rect.XBand(max_valid_x, xhigh * 1e3, keyText=None), zheight=-1, dsn=1)
  
      model_csp = om.rect.ContinuousSteppedPainter(keyText=desc + ' Model')
      model_csp.setFloats(model_x, model_y)
      p.add(model_csp)
  
      for index, (data_csp, lines) in enumerate(zip(data_csps, data_lines)):
          p.add(data_csp, dsn=index + 1)
          p.add(lines, dsn=index + 1)
  
      p.setLabels(data_plot.xlabel, data_plot.ylabel) # data_plot = last one from the for loop
      p.setLinLogAxes(True, False)
      p.setBounds (xlow, xhigh)
      return p, xlow, xhigh