          if datapoints <= (self.args["avg_samples"] * 2):
              logger.info("Not enough data to compile rolling average")
              return avgs
  
          for idx in range(0, datapoints):
              if idx < presample or idx >= datapoints - postsample:
                  avgs.append(None)
                  continue
              avg = sum(data[idx - presample:idx + postsample]) / self.args["avg_samples"]
              avgs.append(avg)
          logger.debug("Calculated Average")
          return avgs
  
      def calc_smoothed(self, data):
          """ Smooth the data """
          last = data[0]  # First value in the plot (first time step)
 
</s> remove         if datapoints <= (self.args["avg_samples"] * 2):
 </s> add         if datapoints <= (self._args["avg_samples"] * 2): </s> remove             return avgs
 </s> add             return np.array([], dtype="float64") </s> remove         avgs = list()
        presample = ceil(self.args["avg_samples"] / 2)
        postsample = self.args["avg_samples"] - presample
        datapoints = len(data)
 </s> add         window = self._args["avg_samples"]
        pad = ceil(window / 2)
        datapoints = data.shape[0] </s> remove     def calc_smoothed(self, data):
        """ Smooth the data """
        last = data[0]  # First value in the plot (first time step)
        weight = self.args["smooth_amount"]
        smoothed = list()
        for point in data:
            smoothed_val = last * weight + (1 - weight) * point  # Calculate smoothed value
            smoothed.append(smoothed_val)                        # Save it
            last = smoothed_val                                  # Anchor the last smoothed value

        return smoothed

    @staticmethod
    def calc_trend(data):
        """ Compile trend data """
 </s> add     def _calc_smoothed(self, data):
        """ Smooth the data.

        Parameters
        ----------
        data: :class:`numpy.ndarray`
            The data to smoothen

        Returns
        -------
        :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.

        Parameters
        ----------
        data: :class:`numpy.ndarray`
            The data to calculate the trend for

        Returns
        -------
        :class:`numpy.ndarray`
            The trend for the given data
        """ </s> remove     def calc_avg(self, data):
        """ Calculate rolling average """
 </s> add         Parameters
        ----------
        data: :class:`numpy.ndarray`
            The data to calculate the moving average for

        Returns
        -------
        :class:`numpy.ndarray`
            The moving average for the given data
        """
        avgs = np.cumsum(data, dtype="float64")
        avgs[window:] = avgs[window:] - avgs[:-window]
        avgs = avgs[window - 1:] / window
        avgs = np.pad(avgs, (pad, datapoints - (avgs.shape[0] + pad)), constant_values=(np.nan,))
        logger.debug("Calculated Average: shape: %s", avgs.shape)
          if datapoints <= (self.args["avg_samples"] * 2):
              logger.info("Not enough data to compile rolling average")
              return avgs
  
        avgs = np.cumsum(data, dtype="float64")
        avgs[window:] = avgs[window:] - avgs[:-window]
        avgs = avgs[window - 1:] / window
        avgs = np.pad(avgs, (pad, datapoints - (avgs.shape[0] + pad)), constant_values=(np.nan,))
        logger.debug("Calculated Average: shape: %s", avgs.shape)
        avgs = np.cumsum(data, dtype="float64")
        avgs[window:] = avgs[window:] - avgs[:-window]
        avgs = avgs[window - 1:] / window
        avgs = np.pad(avgs, (pad, datapoints - (avgs.shape[0] + pad)), constant_values=(np.nan,))
        logger.debug("Calculated Average: shape: %s", avgs.shape)
        avgs = np.cumsum(data, dtype="float64")
        avgs[window:] = avgs[window:] - avgs[:-window]
        avgs = avgs[window - 1:] / window
        avgs = np.pad(avgs, (pad, datapoints - (avgs.shape[0] + pad)), constant_values=(np.nan,))
        logger.debug("Calculated Average: shape: %s", avgs.shape)
        avgs = np.cumsum(data, dtype="float64")
        avgs[window:] = avgs[window:] - avgs[:-window]
        avgs = avgs[window - 1:] / window
        avgs = np.pad(avgs, (pad, datapoints - (avgs.shape[0] + pad)), constant_values=(np.nan,))
        logger.debug("Calculated Average: shape: %s", avgs.shape)
        avgs = np.cumsum(data, dtype="float64")
        avgs[window:] = avgs[window:] - avgs[:-window]
        avgs = avgs[window - 1:] / window
        avgs = np.pad(avgs, (pad, datapoints - (avgs.shape[0] + pad)), constant_values=(np.nan,))
        logger.debug("Calculated Average: shape: %s", avgs.shape)
        avgs = np.cumsum(data, dtype="float64")
        avgs[window:] = avgs[window:] - avgs[:-window]
        avgs = avgs[window - 1:] / window
        avgs = np.pad(avgs, (pad, datapoints - (avgs.shape[0] + pad)), constant_values=(np.nan,))
        logger.debug("Calculated Average: shape: %s", avgs.shape)
        avgs = np.cumsum(data, dtype="float64")
        avgs[window:] = avgs[window:] - avgs[:-window]
        avgs = avgs[window - 1:] / window
        avgs = np.pad(avgs, (pad, datapoints - (avgs.shape[0] + pad)), constant_values=(np.nan,))
        logger.debug("Calculated Average: shape: %s", avgs.shape)
          return avgs
  
      def calc_smoothed(self, data):
          """ Smooth the data """
          last = data[0]  # First value in the plot (first time step)
keep keep keep keep replace replace replace replace replace replace replace keep keep keep keep keep
 GUI - Stats optimization (#1067)

* Faster stat loading + caching

* Compress data in cache

* Optimize some calculations

* Vectorize smoothing

* stats.Calculations optimized

* Load latest training data from live iterator

* Add options to training graph 
lib/gui/stats.py