stats/inspiral_extrinsics.py: pass psd file and initial snr ratios through...

stats/inspiral_extrinsics.py: pass psd file and initial snr ratios through correctly. Previously failed because it was required but not initialized when read from h5

gstlal-inspiral/python/stats/inspiral_extrinsics.py

@@ -1168,7 +1168,7 @@ class TimePhaseSNR(object):
 	locations = {"H1":lal.CachedDetectors[lal.LHO_4K_DETECTOR].location, "L1":lal.CachedDetectors[lal.LLO_4K_DETECTOR].location, "V1":lal.CachedDetectors[lal.VIRGO_DETECTOR].location, "K1":lal.CachedDetectors[lal.KAGRA_DETECTOR].location}
 	numchunks = 20
 
-	def __init__(self, instruments = ("H1", "L1", "V1", "K1"), transtt = None, transtp = None, transpt = None, transpp = None, transdd = None, norm = None, tree_data = None, margsky = None, verbose = False, margstart = 0, margstop = None, SNR=None, psd_fname=None):
+	def __init__(self, instruments = ("H1", "L1", "V1", "K1"), transtt = None, transtp = None, transpt = None, transpp = None, transdd = None, norm = None, tree_data = None, margsky = None, verbose = False, margstart = 0, margstop = None, snr=None, psd_fname=None):
 		"""
 		Initialize a new class from scratch via explicit computation
 		of the tree data and marginalized probability distributions or by providing
@@ -1252,7 +1252,7 @@ class TimePhaseSNR(object):
 		self.tree_data = tree_data
 		self.margsky = margsky
 
-		self.snr = SNR
+		self.snr = snr
 		self.psd_fname = psd_fname
 
 		if self.tree_data is None:
@@ -1306,14 +1306,14 @@ class TimePhaseSNR(object):
 		h5_transpp = f.create_group("transpp")
 		h5_transdd = f.create_group("transdd")
 		h5_norm = f.create_group("norm")
+		h5_snr = f.create_group("SNR")
 		for group, mat in zip((h5_transtt, h5_transtp, h5_transpt, h5_transpp, h5_transdd, h5_norm), (self.transtt, self.transtp, self.transpt, self.transpp, self.transdd, self.norm)):
 			for k,v in mat.items():
 				group.create_dataset(",".join(sorted(k)), data = float(v))
 
-		f.create_dataset("psd", data=self.psd_fname)
-		h5_snr = f.create_group("SNR")
-		for ifo in self.snr:
-			h5_snr.create_dataset(ifo, data=self.snr[ifo].value)
+		f.create_dataset("psd", (1,), data=self.psd_fname)
+		for ifo, v in self.snr.items():
+			h5_snr.create_dataset(",".join(sorted(ifo)), data=v)
 
 		f.close()
 
@@ -1330,6 +1330,9 @@ class TimePhaseSNR(object):
 		transpp = dict((frozenset(k.split(",")), numpy.array(f["transpp"][k])) for k in f["transpp"])
 		transdd = dict((frozenset(k.split(",")), numpy.array(f["transdd"][k])) for k in f["transdd"])
 		norm = dict((frozenset(k.split(",")), numpy.array(f["norm"][k])) for k in f["norm"])
+		snr = dict((frozenset(k.split(",")), numpy.array(f["SNR"][k][:])) for k in f["SNR"])
+
+		psd_fname = f["psd"][0]
 
 		try:
 			dgrp = f["gstlal_extparams"]
@@ -1354,7 +1357,7 @@ class TimePhaseSNR(object):
 				margsky[combo] += numpy.array(dgrp["marg"][key])
 			f.close()
 
-		return cls(instruments = instruments, transtt = transtt, transtp = transtp, transpt = transpt, transpp = transpp, transdd = transdd, norm = norm, tree_data = tree_data, margsky = margsky, **kwargs)
+		return cls(instruments = instruments, transtt = transtt, transtp = transtp, transpt = transpt, transpp = transpp, transdd = transdd, norm = norm, tree_data = tree_data, margsky = margsky, snr = snr, psd_fname = psd_fname, **kwargs)
 
 	@property
 	def combos(self):
@@ -1459,7 +1462,7 @@ class TimePhaseSNR(object):
 		# order is dt, dphi and effective distance ratio for each combination
 		# NOTE the instruments argument here really needs to come from calling instrument_pairs()
 		if hasattr(list(time.values())[0], "__iter__"):
-			outlen = len(time.values()[0])
+			outlen = len(list(time.values())[0])
 		else:
 			outlen =1
 		out = numpy.zeros((outlen, 1 + max(sum(slices.values(),[]))), dtype="float32")