          -------
          :class:`numpy.ndarray`
              The smoothed data
          """
          return _ExponentialMovingAverage(data, self._args["smooth_amount"])()
  
      @classmethod
      def _calc_trend(cls, data):
          """ Calculate polynomial trend of the given data.
  
 
</s> remove     def _calc_trend(cls, data):
 </s> add     def _calc_trend(cls, data: np.ndarray) -> np.ndarray: </s> remove     def _calc_rate_total(cls):
 </s> add     def _calc_rate_total(cls) -> np.ndarray: </s> remove     def start_iteration(self):
 </s> add     def start_iteration(self) -> int: </s> remove     def _calc_smoothed(self, data):
 </s> add     def _calc_smoothed(self, data: np.ndarray) -> np.ndarray: </s> remove     def _get_max_row_size(self):
 </s> add     def _get_max_row_size(self) -> int:
        retval = _ExponentialMovingAverage(data, self._args["smooth_amount"])()
        logger.debug("Calculated Smoothed data: shape: %s", retval.shape)
        return retval
          -------
          :class:`numpy.ndarray`
              The smoothed data
          """
        retval = _ExponentialMovingAverage(data, self._args["smooth_amount"])()
        logger.debug("Calculated Smoothed data: shape: %s", retval.shape)
        return retval
  
      @classmethod
      def _calc_trend(cls, data):
          """ Calculate polynomial trend of the given data.
  
keep keep keep keep replace keep keep keep keep keep
 Bugfixes:
  - Stats graph - Handle NaNs in data
  - logger - de-elevate matplotlib font messages 
lib/gui/analysis/stats.py