Add statistic correction option

pycbc/events/stat.py

@@ -963,9 +963,16 @@ def __init__(self, sngl_ranking, files=None, ifos=None, **kwargs):
         # This will be used to keep track of the template number being used
         self.curr_tnum = None
 
+        # Applies a constant offset to all statistic values in a given instance.
+        # This can be used to e.g. change relative rankings between different
+        # event types. Default is zero offset.
+        self.stat_correction = float(
+            self.kwargs.get("statistic_correction", 0)
+        )
+
         # Go through the keywords and add class information as needed:
         if self.kwargs["sensitive_volume"]:
-            # Add network sensitivity beckmark
+            # Add network sensitivity benchmark
             self.single_dtype.append(("benchmark_logvol", numpy.float32))
             # benchmark_logvol is a benchmark sensitivity array
             # over template id
@@ -1593,6 +1600,9 @@ def rank_stat_single(self, single_info):
         # Combine the signal and noise rates
         loglr = ln_s - ln_noise_rate
 
+        # Apply statistic correction
+        loglr += self.stat_correction
+
         # cut off underflowing and very small values
         loglr[loglr < self.min_stat] = self.min_stat
         return loglr
@@ -1678,6 +1688,9 @@ def rank_stat_coinc(
         # Combine the signal and noise rates
         loglr = ln_s - ln_noise_rate
 
+        # Apply statistic correction
+        loglr += self.stat_correction
+
         # cut off underflowing and very small values
         loglr[loglr < self.min_stat] = self.min_stat
 
@@ -1757,6 +1770,9 @@ def coinc_lim_for_thresh(
         # Combine the signal and noise rates
         loglr = ln_s - ln_noise_rate
 
+        # Apply statistic correction
+        loglr += self.stat_correction
+
         # From this combined rate, what is the minimum snglstat value
         # in the pivot IFO needed to reach the threshold?
         return loglr - thresh