snglcoinc: new LR .__call__() API

- rely on *args, **kwargs instead of passing a "params" blob around

lalburst/python/lalburst/ligolw_burca2.py

@@ -51,7 +51,7 @@ from git_version import version as __version__
 #
 
 
-def assign_likelihood_ratios(connection, coinc_def_id, offset_vectors, vetoseglists, events_func, veto_func, ln_likelihood_ratio_func, likelihood_params_func, verbose = False, params_func_extra_args = ()):
+def assign_likelihood_ratios(connection, coinc_def_id, offset_vectors, vetoseglists, events_func, veto_func, ln_likelihood_ratio_func, verbose = False):
 	"""
 	Assigns likelihood ratio values to coincidences.
 	"""
@@ -77,8 +77,7 @@ def assign_likelihood_ratios(connection, coinc_def_id, offset_vectors, vetosegli
 
 	def ln_likelihood_ratio(coinc_event_id, time_slide_id):
 		try:
-			params = likelihood_params_func([event for event in events_func(cursor, coinc_event_id) if veto_func(event, vetoseglists)], offset_vectors[time_slide_id], *params_func_extra_args)
-			return ln_likelihood_ratio_func(params) if params is not None else None
+			return ln_likelihood_ratio_func([event for event in events_func(cursor, coinc_event_id) if veto_func(event, vetoseglists)], offset_vectors[time_slide_id])
 		except:
 			traceback.print_exc()
 			raise
@@ -109,7 +108,7 @@ WHERE
 	cursor.close()
 
 
-def assign_likelihood_ratios_xml(xmldoc, coinc_def_id, offset_vectors, vetoseglists, events_func, veto_func, ln_likelihood_ratio_func, likelihood_params_func, verbose = False, params_func_extra_args = ()):
+def assign_likelihood_ratios_xml(xmldoc, coinc_def_id, offset_vectors, vetoseglists, events_func, veto_func, ln_likelihood_ratio_func, verbose = False):
 	"""
 	Assigns likelihood ratio values to coincidences (XML version).
 	"""
@@ -129,8 +128,7 @@ def assign_likelihood_ratios_xml(xmldoc, coinc_def_id, offset_vectors, vetosegli
 			progressbar.increment()
 		if coinc_event.coinc_def_id != coinc_def_id:
 			continue
-		params = likelihood_params_func([event for event in events_func(None, coinc_event.coinc_event_id) if veto_func(event, vetoseglists)], offset_vectors[coinc_event.time_slide_id], *params_func_extra_args)
-		coinc_event.likelihood = ln_likelihood_ratio_func(params) if params is not None else None
+		coinc_event.likelihood = ln_likelihood_ratio_func([event for event in events_func(None, coinc_event.coinc_event_id) if veto_func(event, vetoseglists)], offset_vectors[coinc_event.time_slide_id])
 
 	del progressbar
 
@@ -166,11 +164,9 @@ def sngl_burst_veto_func(event, vetoseglists):
 	return event.ifo not in vetoseglists or event.peak not in vetoseglists[event.ifo]
 
 
-def ligolw_burca2(database, ln_likelihood_ratio, params_func, verbose = False, params_func_extra_args = ()):
+def ligolw_burca2(database, ln_likelihood_ratio_func, verbose = False):
 	"""
 	Assigns likelihood ratio values to excess power coincidences.
-	database is lalburst.SnglBurstUtils.CoincDatabase instance, and
-	ln_likelihood_ratio is a LnLikelihoodRatio class instance.
 	"""
 	#
 	# Run core function
@@ -183,10 +179,8 @@ def ligolw_burca2(database, ln_likelihood_ratio, params_func, verbose = False, p
 		vetoseglists = database.vetoseglists,
 		events_func = lambda cursor, coinc_event_id: sngl_burst_events_func(cursor, coinc_event_id, database.sngl_burst_table.row_from_cols),
 		veto_func = sngl_burst_veto_func,
-		ln_likelihood_ratio_func = ln_likelihood_ratio,
-		likelihood_params_func = params_func,
-		verbose = verbose,
-		params_func_extra_args = params_func_extra_args
+		ln_likelihood_ratio_func = ln_likelihood_ratio_func,
+		verbose = verbose
 	)
 
 	#

lalburst/python/lalburst/snglcoinc.py

@@ -1535,12 +1535,12 @@ class LnLRDensity(object):
 	histogram of the candidates observed in a search.
 
 	Typically, the ranking statistic implementation will provide a
-	function to transform a candidate to a "params" object for use with
-	the .__call__() implementation, and so in this way a LnLRDensity
-	object is generally only meaningful in the context of the ranking
+	function to transform a candidate to the arguments to use with the
+	.__call__() implementation, and so in this way a LnLRDensity object
+	is generally only meaningful in the context of the ranking
 	statistic class for which it has been constructed.
 	"""
-	def __call__(self, params):
+	def __call__(self, *args, **kwargs):
 		"""
 		Evaluate.  Return the natural logarithm of the density
 		evaluated at the given parameters.
@@ -1553,10 +1553,10 @@ class LnLRDensity(object):
 		"""
 		raise NotImplementedError
 
-	def increment(self, params, weight = 1.0):
+	def increment(self, *args, **kwargs):
 		"""
-		Increment the counts defining this density by weight
-		(default = 1) at the given parameters.
+		Increment the counts defining this density at the given
+		parameters.
 		"""
 		raise NotImplementedError
 
@@ -2120,7 +2120,7 @@ class LnLikelihoodRatioMixin(object):
 				warnings.warn("inf/inf encountered")
 		return  lnP_signal - lnP_noise
 
-	def ln_lr_samples(self, random_params_seq, sampler_coinc_params = None, **kwargs):
+	def ln_lr_samples(self, random_params_seq, sampler_coinc_params = None):
 		"""
 		Generator that yields an unending sequence of 3-element
 		tuples.  Each tuple's elements are a value of the natural
@@ -2139,15 +2139,16 @@ class LnLikelihoodRatioMixin(object):
 		the two populations.
 
 		random_params_seq is a sequence (generator is OK) yielding
-		2-element tuples whose first element is a choice of
-		parameter values and whose second element is the natural
+		3-element tuples whose first two elements provide the *args
+		and **kwargs values passed to the numerator and denominator
+		density functions, and whose thrid element is the natural
 		logarithm of the probability density from which the
 		parameters have been drawn evaluated at the parameters.
 
-		On each iteration, the sample of parameter values yielded
-		by random_params_seq is passed to our own .__call__()
-		method to evalute the log likelihood ratio at that choice
-		of parameter values.  If sampler_coinc_params is None the
+		On each iteration, the *args and **kwargs values yielded by
+		random_params_seq is passed to our own .__call__() method
+		to evalute the log likelihood ratio at that choice of
+		parameter values.  If sampler_coinc_params is None the
 		parameters are also passed to the .__call__() mehods of the
 		.numerator and .denominator attributes of self to obtain
 		the signal and noise population densities at those
@@ -2166,7 +2167,7 @@ class LnLikelihoodRatioMixin(object):
 		else:
 			lnP_signal_func = sampler_coinc_params.numerator
 			lnP_noise_func = sampler_coinc_params.denominator
-		for params, lnP_params in random_params_seq:
-			lnP_signal = lnP_signal_func(params, **kwargs)
-			lnP_noise = lnP_noise_func(params, **kwargs)
-			yield self(params, **kwargs), lnP_signal - lnP_params, lnP_noise - lnP_params
+		for args, kwargs, lnP_params in random_params_seq:
+			lnP_signal = lnP_signal_func(*args, **kwargs)
+			lnP_noise = lnP_noise_func(*args, **kwargs)
+			yield self(*args, **kwargs), lnP_signal - lnP_params, lnP_noise - lnP_params

lalinspiral/python/lalinspiral/thinca.py

@@ -109,17 +109,14 @@ InspiralCoincDef = lsctables.CoincDef(search = u"inspiral", search_coinc_type =
 
 
 class InspiralCoincTables(snglcoinc.CoincTables):
-	def __init__(self, xmldoc, likelihood_func = None, likelihood_params_func = None):
+	def __init__(self, xmldoc, likelihood_func = None):
 		snglcoinc.CoincTables.__init__(self, xmldoc)
 
 		#
 		# configure the likelihood ratio evaluator
 		#
 
-		if likelihood_func is None and likelihood_params_func is not None or likelihood_func is not None and likelihood_params_func is None:
-			raise ValueError("must provide both a likelihood function and a parameter function or neither")
 		self.likelihood_func = likelihood_func
-		self.likelihood_params_func = likelihood_params_func
 
 		#
 		# find the coinc_inspiral table or create one if not found
@@ -177,7 +174,7 @@ class InspiralCoincTables(snglcoinc.CoincTables):
 		#
 
 		if self.likelihood_func is not None:
-			coinc.likelihood = self.likelihood_func(self.likelihood_params_func(events, offsetvector))
+			coinc.likelihood = self.likelihood_func(events, offsetvector)
 
 		#
 		# done
@@ -343,7 +340,6 @@ def ligolw_thinca(
 	seglists = None,
 	veto_segments = None,
 	likelihood_func = None,
-	likelihood_params_func = None,
 	min_instruments = 2,
 	min_log_L = None,
 	verbose = False
@@ -363,7 +359,7 @@ def ligolw_thinca(
 
 	if verbose:
 		print("indexing ...", file=sys.stderr)
-	coinc_tables = InspiralCoincTables(xmldoc, likelihood_func = likelihood_func, likelihood_params_func = likelihood_params_func)
+	coinc_tables = InspiralCoincTables(xmldoc, likelihood_func = likelihood_func)
 	coinc_def_id = ligolw_coincs.get_coinc_def_id(xmldoc, coinc_definer_row.search, coinc_definer_row.search_coinc_type, create_new = True, description = coinc_definer_row.description)
 	instruments = set(coinc_tables.time_slide_table.getColumnByName("instrument"))