variables to reject abnormal noise

hax/treemakers/common.py

@@ -2,7 +2,7 @@
 """
 from hax.minitrees import TreeMaker
 from collections import defaultdict
-
+import numpy as np
 
 class Fundamentals(TreeMaker):
     """Simple minitree containing basic information about every event, regardless of its contents.
@@ -23,6 +23,45 @@ def extract_data(self, event):
                     event_duration=event.stop_time - event.start_time)
 
 
+class HitRejection(TreeMaker):
+    """Extra information, mainly motivated by cuts used for the first science run.
+    If there are no interactions in the event, all these values will be NaN.
+
+    Provides:
+     - n_channels_rejected: number of channels that has rejected hits
+     - n_hits_rejected: total number of rejected hits
+     - n_lone_hits: total number of lone hits, after reject noise plugin
+     - n_lone_hits_channels: total number of channels that has lone hit, after reject noise plugin
+     - n_lone_hits_before: total number of lone hits, before reject noise plugin
+     - n_lone_hits_channels_before: total number of channels that has lone hit, before reject noise plugin
+    """
+    __version__ = '0.1'
+    branch_selection = ['n_hits_rejected*', 'lone_hits_per_channel_before*', 'lone_hits_per_channel*']
+
+    def extract_data(self, event):
+        result = dict()
+
+        # Rejected hits
+        n_hits_rejected = event.n_hits_rejected
+        n_hits_rejected_flag = [it > 0 for it in n_hits_rejected]
+        result['n_channels_rejected'] = np.sum(n_hits_rejected_flag)
+        result['n_hits_rejected'] = np.sum(n_hits_rejected)
+
+        # lone hit after rejection
+        lone_hits_per_channel = event.lone_hits_per_channel
+        result['n_lone_hits'] = np.sum(lone_hits_per_channel)
+        lone_hits_per_channel_flag = [it > 0 for it in lone_hits_per_channel]
+        result['n_lone_hits_channels'] = np.sum(lone_hits_per_channel_flag)
+
+        # lone hit before rejection
+        lone_hits_per_channel_before = event.lone_hits_per_channel_before
+        result['n_lone_hits_before'] = np.sum(lone_hits_per_channel_before)
+        lone_hits_per_channel_flag_before = [it > 0 for it in lone_hits_per_channel_before]
+        result['n_lone_hits_channels_before'] = np.sum(lone_hits_per_channel_flag_before)
+
+        return result
+
+
 class Extended(TreeMaker):
     """Extra information, mainly motivated by cuts used for the first science run.
     If there are no interactions in the event, all these values will be NaN.