Add ability to rewrite signal model through create_prior_diststats for rerank

gstlal-inspiral/bin/gstlal_inspiral_create_prior_diststats

@@ -230,7 +230,7 @@ else:
 	# fill in the numerator if we won't be making a new one
 	if options.df is None:
 		rankingstat.numerator.densities = old_numerator.densities
-		rankingstat.numerator.horizon_history = old_numerator.horizon_history
+	rankingstat.numerator.horizon_history = old_numerator.horizon_history
 
 
 #

gstlal-inspiral/bin/gstlal_inspiral_workflow

@@ -116,6 +116,16 @@ elif args.command == "create":
 			inj_triggers = find_online(config.rank.inj_triggers_name, start = int(config.start), end = bin_dict, root = config.data.analysis_dir, svd_bins = bin_dict.keys(), subtype = "TRIGGERS")
 			## inverted segments are used as vetos for when the original analysis was off
 			#inverted_segments = find_online(config.rank.segments_name, root = os.path.join(config.data.rerank_dir, 'inverted_segments'), subtype = "TRIGGERS")
+
+			if config.prior.seed_likelihood:
+				ref_psd = None
+				svd_bank = None
+				if config.prior.seed_likelihood.df:
+					svd_bank = DataCache.find(DataType.SVD_BANK, root=filter_dir, svd_bins="*")
+					if config.prior.seed_likelihood.df == "bandwidth":
+						ref_psd = DataCache.find(DataType.REFERENCE_PSD, root=config.data.rerank_dir)
+				dist_stats = dag.create_prior_seed(dist_stats, svd_bank, ref_psd)
+
 			triggers += inj_triggers
 			triggers = dag.add_and_simplify(triggers)
 			triggers = dag.calc_likelihood(triggers, dist_stats)

gstlal-inspiral/python/dags/layers/inspiral.py

@@ -539,6 +539,47 @@ def create_prior_layer(config, dag, svd_bank_cache, median_psd_cache, dist_stat_
 	return prior_cache
 
 
+def create_prior_seed_layer(config, dag, dist_stat_cache, svd_bank_cache=None, median_psd_cache=None):
+	assert 'seed_likelihood' in config.prior and config.prior.seed_likelihood is not None, "Config must have a seed-likelihood section to call the create_prior_seed_layer"
+	layer = Layer(
+		"gstlal_inspiral_create_prior_diststats",
+		requirements={
+			"request_cpus": 2,
+			"request_memory": 4000,
+			"request_disk": "2GB",
+			**config.condor.submit
+		},
+		transfer_files=config.condor.transfer_files,
+	)
+
+	if config.prior.seed_likelihood.df:
+		assert svd_bank_cache is not None, "svd_bank_cache cannot be None if setting config.prior.seed_likelihood.df"
+		if config.prior.seed_likelihood.df == "bandwidth":
+			assert median_psd_cache is not None, "median_psd_cache cannot be None if setting config.prior.seed_likelihood.df to 'bandwidth'"
+
+	if svd_bank_cache is not None:
+		svd_banks = svd_bank_cache.groupby("bin")
+
+	for svd_bin, dist_stat_file in dist_stat_cache.groupby("bin").items():
+		inputs = [
+			Option("seed-likelihood", dist_stat_file.files),
+			Argument("mass-model", config.prior.mass_model, track=False, suppress=True),
+		]
+		if config.prior.seed_likelihood:
+			inputs += [Option("svd-file", svd_banks[svd_bin].files)]
+			if config.prior.seed_likelihood.df == "bandwidth":
+				inputs += [Option("psd-xml", median_psd_cache.files)]
+
+		layer += Node(
+			arguments = [Option(key, value) for key, value in config.prior.seed_likelihood.items()],
+			inputs = inputs,
+			outputs = Option("write-likelihood", dist_stat_file.files),
+		)
+
+	dag.attach(layer)
+	return dist_stat_cache
+
+
 def marginalize_layer(config, dag, prior_cache, dist_stat_cache):
 	layer = Layer(
 		"gstlal_inspiral_marginalize_likelihood",
@@ -2703,6 +2744,7 @@ def layers():
 		"filter_injections": filter_injections_layer,
 		"aggregate": aggregate_layer,
 		"create_prior": create_prior_layer,
+		"create_prior_seed": create_prior_seed_layer,
 		"calc_pdf": calc_pdf_layer,
 		"marginalize": marginalize_layer,
 		"marginalize_pdf": marginalize_pdf_layer,