docs: def _outlier_rejection(self, params, model, signal, ii):
          """
          Helper function to reject outliers
  
          DRY!
          
          """
          # Z score across repetitions:
          z_score = (params - np.mean(params, 0))/np.std(params, 0)
          # Silence warnings: 
          with warnings.catch_warnings():
              warnings.simplefilter("ignore")
              outlier_idx = np.where(np.abs(z_score)>3.0)[0]
              nan_idx = np.where(np.isnan(params))[0]
              outlier_idx = np.unique(np.hstack([nan_idx, outlier_idx]))
              ii[outlier_idx] = 0
              model[outlier_idx] = np.nan
              signal[outlier_idx] = np.nan
              params[outlier_idx] = np.nan
  
          return model, signal, params, ii