code: def map_to_adjust(self, strain, **params):
          
          # calibration param names
          arg_names = ['delta_fs', 'delta_fc', 'delta_qinv', 'kappa_c',
                       'kappa_tst_re', 'kappa_tst_im', 'kappa_pu_re',
                       'kappa_pu_im']
          # calibration param labels as they exist in config files
          arg_labels = [''.join(['calib_', name]) for name in arg_names]
          # default values for calibration params
          default_values = [0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0]
          # make list of calibration param values
          calib_args = []
          for arg, val in zip(arg_labels, default_values):
              if arg in params:
                  calib_args.append(params[arg])
              else:
                  calib_args.append(val)
          # adjust the strain using calibration param values
          strain_adjusted = self.adjust_strain(strain, delta_fs=calib_args[0],
                                delta_fc=calib_args[1], delta_qinv=calib_args[2],
                                kappa_c=calib_args[3],
                                kappa_tst_re=calib_args[4],
                                kappa_tst_im=calib_args[5],
                                kappa_pu_re=calib_args[6],
                                kappa_pu_im=calib_args[7])
          return strain_adjusted
docs: Map an input dictionary of sampling parameters to the
          adjust_strain function by filtering the dictionary for the
          calibration parameters, then calling adjust_strain.
  
          Parameters
          ----------
          strain : FrequencySeries
              The strain to be recalibrated.
          params : dict
              Dictionary of sampling parameters which includes
              calibration parameters.
  
          Return
          ------
          strain_adjusted : FrequencySeries
              The recalibrated strain.