inspiral_lr: add template_id to ranking stat args

- not currently used, but this plumbing will be needed to add source population models to the ranking statistic

gstlal-inspiral/python/far.py

@@ -189,7 +189,8 @@ class RankingStat(snglcoinc.LnLikelihoodRatioMixin):
 		reference = min(events, key = lambda event: event.end)
 		ref_end, ref_offset = reference.end, offsetvector[reference.ifo]
 		# FIXME:  use a proper ID column when one is available
-		if reference.Gamma0 not in self.template_ids:
+		template_id = reference.Gamma0
+		if template_id not in self.template_ids:
 			raise ValueError("event IDs %s are from the wrong template" % ", ".join(sorted(str(event.event_id) for event in events)))
 		# segment spanned by reference event
 		seg = segments.segment(ref_end - reference.template_duration, ref_end)
@@ -206,6 +207,7 @@ class RankingStat(snglcoinc.LnLikelihoodRatioMixin):
 				snrs = dict((event.ifo, event.snr) for event in events),
 				phase = dict((event.ifo, event.coa_phase) for event in events),
 				dt = dict((event.ifo, float(event.end - ref_end) + offsetvector[event.ifo] - ref_offset) for event in events),
+				template_id = template_id,
 				**dict(("%s_snr_chi" % event.ifo, (event.snr, event.chisq / event.snr**2.)) for event in events)
 			)
 		except (ValueError, AssertionError) as e:

gstlal-inspiral/python/stats/inspiral_lr.py

@@ -253,7 +253,7 @@ class LnSignalDensity(LnLRDensity):
 		# network
 		self.horizon_history = horizonhistory.HorizonHistories((instrument, horizonhistory.NearestLeafTree()) for instrument in self.instruments)
 
-	def __call__(self, segments, snrs, phase, dt, **kwargs):
+	def __call__(self, segments, snrs, phase, dt, template_id, **kwargs):
 		assert frozenset(segments) == self.instruments
 		# FIXME:  remove V1 from consideration.  delete after O2
 		kwargs.pop("V1_snr_chi", None)
@@ -490,7 +490,7 @@ class DatalessLnSignalDensity(LnSignalDensity):
 		# so we're ready to go!
 		self.add_signal_model()
 
-	def __call__(self, segments, snrs, phase, dt, **kwargs):
+	def __call__(self, segments, snrs, phase, dt, template_id, **kwargs):
 		# evaluate P(t) \propto number of templates
 		lnP = math.log(len(self.template_ids))
 
@@ -613,7 +613,7 @@ class LnNoiseDensity(LnLRDensity):
 	def segmentlists(self):
 		return self.triggerrates.segmentlistdict()
 
-	def __call__(self, segments, snrs, phase, dt, **kwargs):
+	def __call__(self, segments, snrs, phase, dt, template_id, **kwargs):
 		assert frozenset(segments) == self.instruments
 		# FIXME:  remove V1 from consideration.  delete after O2
 		kwargs.pop("V1_snr_chi", None)
@@ -858,7 +858,7 @@ class DatalessLnNoiseDensity(LnNoiseDensity):
 		mchirp = 0.8
 		self.add_noise_model(number_of_events = 10000000, prefactors_range = ((1. / mchirp)**.33, 25.), df = 40, inv_snr_pow = 3.)
 
-	def __call__(self, segments, snrs, phase, dt, **kwargs):
+	def __call__(self, segments, snrs, phase, dt, template_id, **kwargs):
 		# assume all instruments are on, 1 trigger per second per
 		# template
 		triggers_per_second_per_template = dict.fromkeys(segments, 1.)