From f13fad7a8fb3ae85f0cbe736f9177ec7b8d86436 Mon Sep 17 00:00:00 2001
From: Chad Hanna <chad.hanna@ligo.org>
Date: Wed, 13 Jun 2018 19:43:36 -0400
Subject: [PATCH] inspiral_extrinsics: make sigma in TimePhaseSNR a class
variable
---
.../python/stats/inspiral_extrinsics.py | 47 +++++++++++++++++--
1 file changed, 44 insertions(+), 3 deletions(-)
diff --git a/gstlal-inspiral/python/stats/inspiral_extrinsics.py b/gstlal-inspiral/python/stats/inspiral_extrinsics.py
index 728d2ec2d1..5e0c4ed125 100644
--- a/gstlal-inspiral/python/stats/inspiral_extrinsics.py
+++ b/gstlal-inspiral/python/stats/inspiral_extrinsics.py
@@ -133,6 +133,12 @@ in fact it appears that the reimplementation is a bit more sensitive.
.. |O2_O3_O3_cnt_vs_LR| image:: ../images/O2_O3_O3_cnt_vs_LR.png
:width: 400px
+.. |O2_O3_LR_double_vs_triple| image:: ../images/O2_O3_LR_double_vs_triple.png
+ :width: 400px
+
+.. |O2_O3_HVtest| image:: ../images/HVtest.png
+ :width: 400px
+
+-------------------+-------------------------+-------------------------+
| | O2 Code | O3 Code |
+===================+=========================+=========================+
@@ -143,6 +149,40 @@ in fact it appears that the reimplementation is a bit more sensitive.
| **Count vs LR** | |O2_O3_O2_cnt_vs_LR| | |O2_O3_O3_cnt_vs_LR| |
+-------------------+-------------------------+-------------------------+
+Double vs triple found injections
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In this check we ran two analyses:
+
+- An HL only analysis
+- An HLV analysis
+
+Both were over the same time period. A common set of found injections was
+identified and the false alarm probability (FAP) was computed for the doubles
+and triples. *Ideally* false alarm probabilities for all doubles would be
+higher than all triples, but this is not a requirement since noise and
+especially glitches can cause a triple to be ranked below a double. The plot
+below shows that at least the trend is correct. NOTE we are not currently
+computing doubles and triples and picking the best.
+
+|O2_O3_LR_double_vs_triple|
+
+Check of PDFs
+^^^^^^^^^^^^^
+
+We tested the procedure for evaluating the probabilility using the procedure described below (orange) against a monte carlo (blue). The details are in this script
+
+https://git.ligo.org/lscsoft/gstlal/blob/master/gstlal-inspiral/tests/dtdphitest.py
+
+You can modify this source code to implement different checks. The one
+implemented is to plot the marginal distributions of time delay and phase delay
+between Hanford and Virgo under the condition that the measured effective
+distance is the same. NOTE this test assumes the same covariance matrix for
+noise as the code below in order to test the procedure, but it doesn't prove
+that the assumption is optimal.
+
+|O2_O3_HVtest|
+
Review Status
-------------
@@ -1063,6 +1103,10 @@ 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
+ # FIXME compute this more reliably or expose it as a property
+ # or something
+ sigma = {"time": 0.001, "phase": numpy.pi / 6, "deff": 0.2}
+
def __init__(self, tree_data = None, margsky = None, verbose = False, margstart = 0, margstop = None):
"""
Initialize a new class from scratch via explicit computation
@@ -1072,9 +1116,6 @@ class TimePhaseSNR(object):
from_hdf() method below.
"""
- # FIXME compute this more reliably or expose it as a property
- # or something
- self.sigma = {"time": 0.001, "phase": numpy.pi / 6, "deff": 0.2}
self.norm = (4 * numpy.pi**2)**2
self.tree_data = tree_data
self.margsky = margsky
--
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