far.py: Added ready_for_extinction method

gstlal-inspiral/python/far.py

@@ -636,6 +636,10 @@ class RankingStatPDF(object):
 		self.signal_lr_lnpdf.array *= rankingstat.numerator.candidate_count_model() / self.signal_lr_lnpdf.array.sum()
 		self.signal_lr_lnpdf.normalize()
 
+		self.extinction_fitting_limits = (1/2., 1/100.)
+		# extinction curve fitting is done for the range of LRs
+		# corresponding to the 1/2 and 1/100 point of the zl CCDF
+
 
 	def copy(self):
 		new = self.__class__(None)
@@ -713,6 +717,33 @@ WHERE
 		return self
 
 
+	def ready_for_extinction(self):
+		# ensure we have sufficient zerolag and noise samples before attempting the extinction curve fit
+		# none of the checks below should evaluate to True even with a small amount of zerolag and noise
+		# samples. They should only evaluate to True at the start of an online analysis
+		bg = self.noise_lr_lnpdf.copy().array
+		fg = self.zero_lag_lr_lnpdf.copy().array
+		bg[:10] = 0.
+		fg[:10] = 0.
+		if fg.sum() == 0 or bg.sum() == 0:
+			return False
+
+		fg_ccdf = numpy.cumsum(fg[::-1])[::-1]
+		ix_min = (fg_ccdf < fg_ccdf[0] * self.extinction_fitting_limits[0]).argmax()
+		ix_max = (fg_ccdf < fg_ccdf[0] * self.extinction_fitting_limits[1]).argmax()
+		if ix_min == ix_max:
+			return False
+
+		if fg[ix_min: ix_max + 1].sum() == 0 or bg[ix_min: ix_max + 1].sum() == 0:
+			return False
+
+		if (fg_ccdf[ix_min: ix_max + 1] == 0).any():
+			# log will evaluate to -inf, and the curve fitting will crash
+			return False
+
+		return True
+
+
 	def new_with_extinction(self, verbose = False):
 		self = self.copy()
 
@@ -732,8 +763,8 @@ WHERE
 
 		# fitting is done between ix_min and ix_max
 		fg_ccdf = numpy.cumsum(fg[::-1])[::-1]
-		ix_min = (fg_ccdf < fg_ccdf[0] / 2.).argmax()
-		ix_max = (fg_ccdf < fg_ccdf[0] / 100.).argmax()
+		ix_min = (fg_ccdf < fg_ccdf[0] * self.extinction_fitting_limits[0]).argmax()
+		ix_max = (fg_ccdf < fg_ccdf[0] * self.extinction_fitting_limits[1]).argmax()
 
 
 		bgtotal = bg[ix_min: ix_max + 1].sum()