Add some comments concerning performance, probably not much to improve

eos/event_data_types.py

@@ -46,6 +46,8 @@ EVENT_BITMASKS = {
     }
 
 def append_fields(input: np.recarray, new_fields: List[Tuple[str, np.dtype]]):
+    # TODO: This action is used often and time consuming as it runs len(flds) times over all indices.
+    #       Could only be faster if array is allocated in the beginning with all fields, less flexible.
     # add one ore more fields to a recarray, numpy functions seems to fail
     flds = [(name, dtypei[0]) for name, dtypei in input.dtype.fields.items()]
     flds += new_fields

eos/event_handling.py

@@ -165,7 +165,9 @@ class ApplyMask(EventDataAction):
         self.bitmask_filter = bitmask_filter
 
     def perform_action(self, dataset: EventDatasetProtocol) ->None:
-        # TODO: why is this action time consuming?
+        # TODO: Most time in test examples is spend here.
+        #       While the actions here are very simple, they act on a large array,
+        #       so even just comparison and indexing become time consuming.
         d = dataset.data
         pre_filter = d.events.shape[0]
         if logging.getLogger().level <= logging.DEBUG: