docs: def get_bright(mask, image, ret_data="avg,sd"):
      """Compute avg and/or std of the event brightness
  
      The event brightness is defined by the gray-scale values of the
      image data within the event mask area.
  
      Parameters
      ----------
      mask: ndarray or list of ndarrays of shape (M,N) and dtype bool
          The mask values, True where the event is located in `image`.
      image: ndarray or list of ndarrays of shape (M,N)
          A 2D array that holds the image in form of grayscale values
          of an event.
      ret_data: str
          A comma-separated list of metrices to compute
          - "avg": compute the average
          - "sd": compute the standard deviation
          Selected metrics are returned in alphabetical order.
  
      Returns
      -------
      bright_avg: float or ndarray of size N
          Average image data within the contour
      bright_std: float or ndarray of size N
          Standard deviation of image data within the contour
      """
      # This method is based on a pull request by Maik Herbig.
      ret_avg = "avg" in ret_data
      ret_std = "sd" in ret_data
  
      if ret_avg + ret_std == 0:
          raise ValueError("No valid metrices selected!")
  
      if isinstance(mask, np.ndarray) and len(mask.shape) == 2:
          # We have a single image
          image = [image]
          mask = [mask]
          ret_list = False
      else:
          ret_list = True
  
      length = min(len(mask), len(image))
  
      # Results are stored in a separate array initialized with nans
      if ret_avg:
          avg = np.zeros(length, dtype=float) * np.nan
      if ret_std:
          std = np.zeros(length, dtype=float) * np.nan
  
      for ii in range(length):
          imgi = image[ii]
          mski = mask[ii]
          # Assign results
          if ret_avg:
              avg[ii] = np.mean(imgi[mski])
          if ret_std:
              std[ii] = np.std(imgi[mski])
  
      results = []
      # Keep alphabetical order
      if ret_avg:
          results.append(avg)
      if ret_std:
          results.append(std)
  
      if not ret_list:
          # Only return scalars
          results = [r[0] for r in results]
  
      if ret_avg+ret_std == 1:
          # Only return one column
          return results[0]
  
      return results