far.py: populate_prob_of_instruments_given_signal() no long requires segs

- now that the horizon distances are set to 0 in the histories when the instruments are off, populate_prob_of_instruments_given_signal() no long requires segment lists to do its job

gstlal-inspiral/bin/gstlal_inspiral_calc_likelihood

@@ -146,7 +146,7 @@ if options.verbose:
 	print >>sys.stderr, "total livetime:\n\t%s" % ",\n\t".join("%s = %s s" % (instrument, str(abs(segs))) for instrument, segs in seglists.items())
 
 # compute the probability of instruments given signal
-coincparamsdistributions.populate_prob_of_instruments_given_signal(segs = seglists, n = 1.0, verbose = options.verbose)
+coincparamsdistributions.populate_prob_of_instruments_given_signal(n = 1.0, verbose = options.verbose)
 
 # compute the instrument combination counts
 coincparamsdistributions.add_instrument_combination_counts(segs = seglists, verbose = options.verbose)

gstlal-inspiral/bin/gstlal_inspiral_calc_rank_pdfs

@@ -115,7 +115,7 @@ if options.verbose:
 	print >>sys.stderr, "total livetime:\n\t%s" % ",\n\t".join("%s = %s s" % (instrument, str(abs(segs))) for instrument, segs in seglists.items())
 
 # Compute the probability of instruments given signal
-coincparamsdistributions.populate_prob_of_instruments_given_signal(segs = seglists, n = 1.0, verbose = options.verbose)
+coincparamsdistributions.populate_prob_of_instruments_given_signal(n = 1.0, verbose = options.verbose)
 
 # Compute the intrument combination counts
 coincparamsdistributions.add_instrument_combination_counts(segs = seglists, verbose = options.verbose)

gstlal-inspiral/bin/gstlal_inspiral_recalc_likelihood_with_zerolag

@@ -331,7 +331,7 @@ def get_likelihood_funcs(likelihood_files, options):
 	# Compute the probability of instruments given signal and the
 	# instrument combination counts
 	#
-	coinc_params_distributions.populate_prob_of_instruments_given_signal(segs = seglists, n = 1.0, verbose = options.verbose)
+	coinc_params_distributions.populate_prob_of_instruments_given_signal(n = 1.0, verbose = options.verbose)
 	coinc_params_distributions.add_instrument_combination_counts(segs = seglists, verbose = options.verbose)
 
 	return coinc_params_distributions.coinc_params, snglcoinc.LnLikelihoodRatio(coinc_params_distributions)

gstlal-inspiral/bin/gstlal_ll_inspiral_create_prior_diststats

@@ -186,7 +186,7 @@ for n in range(2, len(horizon_distances) + 1):
 		pdf = rate.InterpBinnedArray(lnbinnedarray, fill_value = float("-inf"))
 		diststats.snr_joint_pdf_cache[key] = pdf, binnedarray, 0
 
-diststats.populate_prob_of_instruments_given_signal(segs)
+diststats.populate_prob_of_instruments_given_signal()
 
 #
 # Finished with this class

gstlal-inspiral/python/far.py

@@ -477,32 +477,17 @@ class ThincaCoincParamsDistributions(snglcoinc.CoincParamsDistributions):
 	def add_foreground_snrchi_prior(self, n, prefactors_range = (0.01, 0.25), df = 40, inv_snr_pow = 4., verbose = False):
 		self.add_snrchi_prior(self.injection_rates, n, prefactors_range, df, inv_snr_pow = inv_snr_pow, verbose = verbose)
 
-	def populate_prob_of_instruments_given_signal(self, segs, n = 1., verbose = False):
+	def populate_prob_of_instruments_given_signal(self, n = 1., verbose = False):
 		#
 		# populate instrument combination binning
 		#
 
-		assert len(segs) > 1
-		assert set(self.horizon_history) <= set(segs)
-
 		# probability that a signal is detectable by each of the
-		# instrument combinations
+		# instrument combinations.  because the horizon distance is
+		# 0'ed when an instrument is off, this marginalization over
+		# horizon distance histories also accounts for duty cycles
 		P = P_instruments_given_signal(self.horizon_history)
 
-		# multiply by probability that enough instruments are on to
-		# form each of those combinations
-		#
-		# FIXME:  if when an instrument is off it has its horizon
-		# distance set to 0 in the horizon history, then this step
-		# will not be needed because the marginalization over
-		# horizon histories will already reflect the duty cycles.
-		P_live = snglcoinc.CoincSynthesizer(segmentlists = segs).P_live
-		for instruments in P:
-			P[instruments] *= sum(sorted(p for on_instruments, p in P_live.items() if on_instruments >= instruments))
-		# renormalize
-		total = sum(sorted(P.values()))
-		for instruments in P:
-			P[instruments] /= total
 		# populate binning from probabilities
 		for instruments, p in P.items():
 			self.injection_rates["instruments"][instruments,] += n * p