From 411d672e732c8ecb0859c5706ebdd9464c456d3e Mon Sep 17 00:00:00 2001
From: Duncan Meacher <duncan.meacher@ligo.org>
Date: Mon, 16 Jul 2018 09:07:18 -0700
Subject: [PATCH] Revert "inspiral_lr.py: Add population model to ranking
statistic. Population model information in inspiral_intrinsics.py."
This reverts commit 1cf67f62b7aa5a6de82aedc1d3ab03e9f41606d7.
---
gstlal-inspiral/python/stats/Makefile.am | 1 -
.../python/stats/inspiral_intrinsics.py | 108 ------------------
gstlal-inspiral/python/stats/inspiral_lr.py | 31 -----
3 files changed, 140 deletions(-)
delete mode 100644 gstlal-inspiral/python/stats/inspiral_intrinsics.py
diff --git a/gstlal-inspiral/python/stats/Makefile.am b/gstlal-inspiral/python/stats/Makefile.am
index 0bc6cd2ef6..aed36e7aac 100644
--- a/gstlal-inspiral/python/stats/Makefile.am
+++ b/gstlal-inspiral/python/stats/Makefile.am
@@ -4,7 +4,6 @@ statsdir = $(pkgpythondir)/stats
stats_PYTHON = \
horizonhistory.py \
inspiral_extrinsics.py \
- inspiral_intrinsics.py \
inspiral_lr.py \
trigger_rate.py
diff --git a/gstlal-inspiral/python/stats/inspiral_intrinsics.py b/gstlal-inspiral/python/stats/inspiral_intrinsics.py
deleted file mode 100644
index 5ec19a72c6..0000000000
--- a/gstlal-inspiral/python/stats/inspiral_intrinsics.py
+++ /dev/null
@@ -1,108 +0,0 @@
-# Copyright (C) 2017,2018 Heather Fong
-#
-# This program is free software; you can redistribute it and/or modify it
-# under the terms of the GNU General Public License as published by the
-# Free Software Foundation; either version 2 of the License, or (at your
-# option) any later version.
-#
-# This program is distributed in the hope that it will be useful, but
-# WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
-# Public License for more details.
-#
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
-
-
-#
-# =============================================================================
-#
-# Preamble
-#
-# =============================================================================
-#
-
-
-import h5py
-import math
-import numpy
-import os
-#from scipy.interpolate import PPoly
-
-
-from gstlal import stats as gstlalstats
-
-
-# FIXME: caution, this information might get organized differently later.
-# for now we just need to figure out where the gstlal-inspiral directory in
-# share/ is. don't write anything that assumes that this module will
-# continue to define any of these symbols
-from gstlal import paths as gstlal_config_paths
-
-
-__all__ = [
- "UniformInTemplatePopulationModel",
- "SourcePopulationModel"
-]
-
-
-#
-# =============================================================================
-#
-# Population Model
-#
-# =============================================================================
-#
-
-
-class UniformInTemplatePopulationModel(object):
- def __init__(self, template_ids):
- """
- Assumes uniform in template population model, no
- astrophysical prior.
- """
- self.lnP = -math.log(len(template_ids))
-
-
- @gstlalstats.assert_ln_probability
- def lnP_template_signal(self, template_id, snr):
- assert snr >= 0.
- return self.lnP
-
-
-class SourcePopulationModel(object):
- DEFAULT_FILENAME = os.path.join(gstlal_config_paths["pkgdatadir"], "lnP_template_signal_BNS_gaussian_lowspin_Ozel.hdf5")
-
-
- def __init__(self, template_ids, filename = None):
- """
- Sets the polynomial coefficients, given the template ID
- and SNR for a source population model, from which
- lnP is then computed using PPoly.
- """
- with h5py.File(filename if filename is not None else self.DEFAULT_FILENAME, 'r') as model:
- coefficients = model['coefficients'].value
- snr_bp = model['SNR'].value
- # PPoly can construct an array of polynomials by just
- # feeding it the coefficients array all in one go, but then
- # it insists on evaluating all of them at once. we don't
- # want to suffer that cost, so we have to make an array of
- # PPoly objects ourselves, and index into it to evaluate
- # just one. since we have to do this anyway, we use a
- # dictionary to also solve the problem of mapping
- # template_id to a specific polynomial
- self.polys = dict((template_id, PPoly(coefficients[:,:,[template_id]], snr_bp)) for template_id in template_ids)
- self.max_snr = snr_bp.max()
-
-
- @gstlalstats.assert_ln_probability
- def lnP_template_signal(self, template_id, snr):
- assert snr >= 0.
- try:
- lnP_vs_snr = self.polys[template_id]
- except KeyError:
- raise KeyError("template ID %d is not in this model" % template_id)
- # PPoly's .__call__() returns an array, so we need the
- # final [0] to flatten it
- return lnP_vs_snr(min(snr, self.max_snr))[0]
diff --git a/gstlal-inspiral/python/stats/inspiral_lr.py b/gstlal-inspiral/python/stats/inspiral_lr.py
index fadd518136..0f7751740a 100644
--- a/gstlal-inspiral/python/stats/inspiral_lr.py
+++ b/gstlal-inspiral/python/stats/inspiral_lr.py
@@ -44,7 +44,6 @@ from glue.ligolw import utils as ligolw_utils
from ligo import segments
from gstlal.stats import horizonhistory
from gstlal.stats import inspiral_extrinsics
-from gstlal.stats import inspiral_intrinsics
from gstlal.stats import trigger_rate
import lal
from lal import rate
@@ -260,17 +259,6 @@ class LnSignalDensity(LnLRDensity):
# record of horizon distances for all instruments in the
# network
self.horizon_history = horizonhistory.HorizonHistories((instrument, horizonhistory.NearestLeafTree()) for instrument in self.instruments)
-
- # source population model
- if self.template_ids:
- self.population_model = inspiral_intrinsics.UniformInTemplatePopulationModel(self.template_ids)
- # FIXME: switch to this when a model file becomes
- # available
- #self.population_model = inspiral_intrinsics.SourcePopulationModel(self.template_ids)
- else:
- # default lnP = 1/len(templates) = 0
- self.population_model = inspiral_intrinsics.UniformInTemplatePopulationModel([0])
-
self.InspiralExtrinsics = inspiral_extrinsics.InspiralExtrinsics(self.min_instruments)
def __call__(self, segments, snrs, phase, dt, template_id, **kwargs):
@@ -322,9 +310,6 @@ class LnSignalDensity(LnLRDensity):
except ValueError:
return float("-inf")
- # evaluate population model
- lnP += self.population_model.lnP_template_signal(template_id, max(snrs.values()))
-
# evalute the (snr, \chi^2 | snr) PDFs (same for all
# instruments)
interp = self.interps["snr_chi"]
@@ -490,19 +475,6 @@ class LnSignalDensity(LnLRDensity):
xml = cls.get_xml_root(xml, name)
self = super(LnSignalDensity, cls).from_xml(xml, name)
self.horizon_history = horizonhistory.HorizonHistories.from_xml(xml, u"horizon_history")
- # source population model
- # FIXME: this should probably be stored in the ranking
- # statistic file somehow. maybe the HDF5 filename could be
- # stored. whatever would allow the correct model to be
- # re-initialized
- if self.template_ids:
- self.population_model = inspiral_intrinsics.UniformInTemplatePopulationModel(self.template_ids)
- # FIXME: switch to this when a model file becomes
- # available
- #self.population_model = inspiral_intrinsics.SourcePopulationModel(self.template_ids)
- else:
- # default lnP = 1/len(templates) = 0
- self.population_model = inspiral_intrinsics.UniformInTemplatePopulationModel([0])
return self
@@ -547,9 +519,6 @@ class DatalessLnSignalDensity(LnSignalDensity):
except ValueError:
return float("-inf")
- # evaluate population model
- lnP += self.population_model.lnP_template_signal(template_id, max(snrs.values()))
-
# evalute the (snr, \chi^2 | snr) PDFs (same for all
# instruments)
interp = self.interps["snr_chi"]
--
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