code: def autointerpret(self, event, step_size=None, calculation_type="DE-BFL"):
          
          if not self.user_warning("This feature is in ALPHA and still in development and testing. It is subject to bugs and will often create a LOT of new interpretations. This feature should only be used to get a general idea of the trend of the data before actually mannuely interpreting the data and the output of this function should certainly not be trusted as 100% accurate and useable for publication. Would you like to continue?"):
              return
          if not self.clear_interpretations():
              return
  
          print("Autointerpretation Start")
  
          self.set_test_mode(True)
          for specimen in self.specimens:
              self.autointerpret_specimen(specimen, step_size, calculation_type)
          self.set_test_mode(False)
  
          if self.pmag_results_data['specimens'][self.s] != []:
              self.current_fit = self.pmag_results_data['specimens'][self.s][-1]
          else:
              self.current_fit = None
          print("Autointerpretation Complete")
          self.update_selection()
          if self.ie_open:
              self.ie.update_editor()
docs: Clears current interpretations and adds interpretations to every
          specimen of type = calculation_type by attempting fits of size =
          step size and type = calculation_type and testing the mad or a95
          then finding peaks in these to note areas of maximum error then fits
          between these peaks excluding them.
  
          Parameters
          ----------
          step_size : int that is the size of fits to make while stepping
              through data if None then step size = len(meas data for
              specimen)/10 rounded up if that value is greater than 3 else it
              is 3 (default: None)
          calculation_type : type of fit to make (default: DE-BFL or line)