From ec3859c19748067f2aef268965c022df739d0e01 Mon Sep 17 00:00:00 2001
From: Kipp Cannon <kipp.cannon@ligo.org>
Date: Tue, 6 Nov 2018 06:54:59 -0800
Subject: [PATCH] inspiral_lr: define NegInf symbol
- avoid evaluating float("-inf") in performance sensitive paths
---
gstlal-inspiral/python/stats/inspiral_lr.py | 19 ++++++++++++-------
1 file changed, 12 insertions(+), 7 deletions(-)
diff --git a/gstlal-inspiral/python/stats/inspiral_lr.py b/gstlal-inspiral/python/stats/inspiral_lr.py
index 415111632c..ee92bd9d05 100644
--- a/gstlal-inspiral/python/stats/inspiral_lr.py
+++ b/gstlal-inspiral/python/stats/inspiral_lr.py
@@ -26,6 +26,11 @@
#
+try:
+ from fpconst import NegInf
+except ImportError:
+ # not all machines have fpconst installed
+ NegInf = float("-inf")
import math
import numpy
import os
@@ -307,7 +312,7 @@ class LnSignalDensity(LnLRDensity):
def __call__(self, segments, snrs, chi2s_over_snr2s, phase, dt, template_id):
assert frozenset(segments) == self.instruments
if len(snrs) < self.min_instruments:
- return float("-inf")
+ return NegInf
# use volume-weighted average horizon distance over
# duration of event to estimate sensitivity
@@ -335,7 +340,7 @@ class LnSignalDensity(LnLRDensity):
# denominator were both normalized properly.
horizon = sorted(horizons.values())[-self.min_instruments] / TYPICAL_HORIZON_DISTANCE
if not horizon:
- return float("-inf")
+ return NegInf
lnP = 3. * math.log(horizon) + math.log(len(self.template_ids))
# Add P(instruments | horizon distances)
@@ -343,14 +348,14 @@ class LnSignalDensity(LnLRDensity):
lnP += math.log(self.InspiralExtrinsics.p_of_instruments_given_horizons(snrs.keys(), horizons))
except ValueError:
# The code raises a value error when a needed horizon distance is zero
- return float("-inf")
+ return NegInf
# Evaluate dt, dphi, snr probability
try:
lnP += math.log(self.InspiralExtrinsics.time_phase_snr(dt, phase, snrs, horizons))
# FIXME need to make sure this is really a math domain error
except ValueError:
- return float("-inf")
+ return NegInf
# evaluate population model
lnP += self.population_model.lnP_template_signal(template_id, max(snrs.values()))
@@ -567,14 +572,14 @@ class DatalessLnSignalDensity(LnSignalDensity):
lnP += math.log(self.InspiralExtrinsics.p_of_instruments_given_horizons(snrs.keys(), horizons))
except ValueError:
# The code raises a value error when a needed horizon distance is zero
- return float("-inf")
+ return NegInf
# Evaluate dt, dphi, snr probability
try:
lnP += math.log(self.InspiralExtrinsics.time_phase_snr(dt, phase, snrs, horizons))
# FIXME need to make sure this is really a math domain error
except ValueError:
- return float("-inf")
+ return NegInf
# evaluate population model
lnP += self.population_model.lnP_template_signal(template_id, max(snrs.values()))
@@ -695,7 +700,7 @@ class LnNoiseDensity(LnLRDensity):
def __call__(self, segments, snrs, chi2s_over_snr2s, phase, dt, template_id):
assert frozenset(segments) == self.instruments
if len(snrs) < self.min_instruments:
- return float("-inf")
+ return NegInf
# FIXME: the +/-3600 s window thing is a temporary hack to
# work around the problem of vetoes creating short segments
--
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