          ``True`` if values significantly away from the average should be excluded, otherwise
          ``False``. Default: ``False``
      """
      def __init__(self, session_id,
                   display="loss",
                   loss_keys="loss",
                   selections="raw",
                   avg_samples=500,
                   smooth_amount=0.90,
                   flatten_outliers=False):
          logger.debug("Initializing %s: (session_id: %s, display: %s, loss_keys: %s, "
                       "selections: %s, avg_samples: %s, smooth_amount: %s, flatten_outliers: %s)",
                       self.__class__.__name__, session_id, display, loss_keys, selections,
                       avg_samples, smooth_amount, flatten_outliers)
  
 
</s> remove     def __init__(self, session_id, data_points):
 </s> add     def __init__(self, session_id: int, data_points: int) -> None: </s> remove     def __init__(self, session):
 </s> add     def __init__(self, session: GlobalSession) -> None: </s> remove         mean = np.mean(data)
        limit = np.std(data)
 </s> add         mean = np.mean(np.nan_to_num(data))
        limit = np.std(np.nan_to_num(data)) </s> remove     def is_training(self):
 </s> add     def is_training(self) -> bool: </s> remove     def _compile_display_data(self):
        """ Compile the data to be displayed. """
 </s> add     def _compile_display_data(self) -> bool:
        """ Compile the data to be displayed.

        Returns
        -------
        bool
            ``True`` if there is valid data to display, ``False`` if not
        """
                 display: str = "loss",
                 loss_keys: str = "loss",
                 selections: str = "raw",
                 avg_samples: int = 500,
                 smooth_amount: float = 0.90,
                 flatten_outliers: bool = False) -> None:
          ``True`` if values significantly away from the average should be excluded, otherwise
          ``False``. Default: ``False``
      """
      def __init__(self, session_id,
                 display: str = "loss",
                 loss_keys: str = "loss",
                 selections: str = "raw",
                 avg_samples: int = 500,
                 smooth_amount: float = 0.90,
                 flatten_outliers: bool = False) -> None:
                 display: str = "loss",
                 loss_keys: str = "loss",
                 selections: str = "raw",
                 avg_samples: int = 500,
                 smooth_amount: float = 0.90,
                 flatten_outliers: bool = False) -> None:
                 display: str = "loss",
                 loss_keys: str = "loss",
                 selections: str = "raw",
                 avg_samples: int = 500,
                 smooth_amount: float = 0.90,
                 flatten_outliers: bool = False) -> None:
                 display: str = "loss",
                 loss_keys: str = "loss",
                 selections: str = "raw",
                 avg_samples: int = 500,
                 smooth_amount: float = 0.90,
                 flatten_outliers: bool = False) -> None:
                 display: str = "loss",
                 loss_keys: str = "loss",
                 selections: str = "raw",
                 avg_samples: int = 500,
                 smooth_amount: float = 0.90,
                 flatten_outliers: bool = False) -> None:
                 display: str = "loss",
                 loss_keys: str = "loss",
                 selections: str = "raw",
                 avg_samples: int = 500,
                 smooth_amount: float = 0.90,
                 flatten_outliers: bool = False) -> None:
          logger.debug("Initializing %s: (session_id: %s, display: %s, loss_keys: %s, "
                       "selections: %s, avg_samples: %s, smooth_amount: %s, flatten_outliers: %s)",
                       self.__class__.__name__, session_id, display, loss_keys, selections,
                       avg_samples, smooth_amount, flatten_outliers)
  
keep keep keep keep replace replace replace replace replace replace keep keep keep keep keep
 Bugfixes:
  - Stats graph - Handle NaNs in data
  - logger - de-elevate matplotlib font messages 
lib/gui/analysis/stats.py