code: def _rm_outlier_by_amp(self, params, model, signal, ii):
          
          maxamps = np.nanmax(np.abs(model),0)
          z_score = (maxamps - np.nanmean(maxamps,0))/np.nanstd(maxamps,0)
          with warnings.catch_warnings():
              warnings.simplefilter("ignore")
              outlier_idx = np.where(np.abs(z_score)>2.0)[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
docs: Helper function to reject outliers based on mean amplitude