From 286011fdf4563668d356767f51c415a80ffc01be Mon Sep 17 00:00:00 2001
From: Colm Talbot <colm.talbot@ligo.org>
Date: Thu, 17 May 2018 11:42:33 +1000
Subject: [PATCH] bbh parameter filling uses likelihood rather than wg and ifos
---
tupak/conversion.py | 70 ++++++++++++++++++++++++---------------------
1 file changed, 38 insertions(+), 32 deletions(-)
diff --git a/tupak/conversion.py b/tupak/conversion.py
index f229d1a07..67e9ba7ac 100644
--- a/tupak/conversion.py
+++ b/tupak/conversion.py
@@ -98,7 +98,7 @@ def convert_to_lal_binary_black_hole_parameters(parameters, search_keys, remove=
return ignored_keys
-def generate_all_bbh_parameters(sample, waveform_generator=None, interferometers=None, priors=None):
+def generate_all_bbh_parameters(sample, likelihood=None, priors=None):
"""
From either a single sample or a set of samples fill in all missing BBH parameters, in place.
@@ -106,24 +106,21 @@ def generate_all_bbh_parameters(sample, waveform_generator=None, interferometers
----------
sample: dict or pandas.DataFrame
Samples to fill in with extra parameters, this may be either an injection or posterior samples.
- waveform_generator: tupak.waveform_generator.WaveformGenerator, optional
- If the waveform generator and interferometers are provided, the SNRs will be recorded.
- interferometers: list, optional
- List of tupak.detector.Interferometer objects.
- If the waveform generator and interferometers are provided, the SNRs will be recorded.
+ likelihood: tupak.likelihood.Likelihood
+ Likelihood used for sampling, used for waveform and likelihood.interferometers.
priors: dict, optional
Dictionary of prior objects, used to fill in non-sampled parameters.
"""
- if waveform_generator is not None:
- sample['reference_frequency'] = waveform_generator.parameters['reference_frequency']
- sample['waveform_approximant'] = waveform_generator.parameters['waveform_approximant']
+ if likelihood is not None:
+ sample['reference_frequency'] = likelihood.waveform_generator.parameters['reference_frequency']
+ sample['waveform_approximant'] = likelihood.waveform_generator.parameters['waveform_approximant']
fill_from_fixed_priors(sample, priors)
convert_to_lal_binary_black_hole_parameters(sample, [key for key in sample.keys()], remove=False)
generate_non_standard_parameters(sample)
generate_component_spins(sample)
- compute_snrs(sample, waveform_generator, interferometers)
+ compute_snrs(sample, likelihood)
def fill_from_fixed_priors(sample, priors):
@@ -194,39 +191,48 @@ def generate_component_spins(sample):
logging.warning("Component spin extraction failed.")
-def compute_snrs(sample, waveform_generator, interferometers):
+def compute_snrs(sample, likelihood):
"""Compute the optimal and matched filter snrs of all posterior samples."""
- temp_sample = sample.copy()
- if waveform_generator is not None and interferometers is not None:
+ temp_sample = sample
+ if likelihood is not None:
if isinstance(temp_sample, dict):
- for key in waveform_generator.parameters.keys():
- waveform_generator.parameters[key] = temp_sample[key]
- signal_polarizations = waveform_generator.frequency_domain_strain()
- for interferometer in interferometers:
- signal = interferometer.get_detector_response(signal_polarizations, waveform_generator.parameters)
+ for key in likelihood.waveform_generator.parameters.keys():
+ likelihood.waveform_generator.parameters[key] = temp_sample[key]
+ signal_polarizations = likelihood.waveform_generator.frequency_domain_strain()
+ for interferometer in likelihood.interferometers:
+ signal = interferometer.get_detector_response(signal_polarizations,
+ likelihood.waveform_generator.parameters)
sample['{}_matched_filter_snr'.format(interferometer.name)] = \
tupak.utils.matched_filter_snr_squared(signal, interferometer,
- waveform_generator.time_duration)**0.5
+ likelihood.waveform_generator.time_duration)**0.5
sample['{}_optimal_snr'.format(interferometer.name)] = tupak.utils.optimal_snr_squared(
- signal, interferometer, waveform_generator.time_duration) ** 0.5
+ signal, interferometer, likelihood.waveform_generator.time_duration) ** 0.5
else:
logging.info('Computing SNRs for every sample, this may take some time.')
- matched_filter_snrs = {interferometer.name: [] for interferometer in interferometers}
- optimal_snrs = {interferometer.name: [] for interferometer in interferometers}
+ all_interferometers = likelihood.interferometers
+ matched_filter_snrs = {interferometer.name: [] for interferometer in all_interferometers}
+ optimal_snrs = {interferometer.name: [] for interferometer in all_interferometers}
+ likelihoods = {interferometer.name: [] for interferometer in all_interferometers}
for ii in range(len(temp_sample)):
- for key in set(temp_sample.keys()).intersection(waveform_generator.parameters.keys()):
- waveform_generator.parameters[key] = temp_sample[key][ii]
- for key in waveform_generator.search_parameter_keys:
- waveform_generator.parameters[key] = temp_sample[key][ii]
- signal_polarizations = waveform_generator.frequency_domain_strain()
- for interferometer in interferometers:
- signal = interferometer.get_detector_response(signal_polarizations, waveform_generator.parameters)
+ for key in set(temp_sample.keys()).intersection(likelihood.waveform_generator.parameters.keys()):
+ likelihood.waveform_generator.parameters[key] = temp_sample[key][ii]
+ for key in likelihood.waveform_generator.sampling_parameter_keys:
+ likelihood.waveform_generator.parameters[key] = temp_sample[key][ii]
+ signal_polarizations = likelihood.waveform_generator.frequency_domain_strain()
+ for interferometer in all_interferometers:
+ signal = interferometer.get_detector_response(signal_polarizations,
+ likelihood.waveform_generator.parameters)
matched_filter_snrs[interferometer.name].append(tupak.utils.matched_filter_snr_squared(
- signal, interferometer, waveform_generator.time_duration)**0.5)
+ signal, interferometer, likelihood.waveform_generator.time_duration)**0.5)
optimal_snrs[interferometer.name].append(tupak.utils.optimal_snr_squared(
- signal, interferometer, waveform_generator.time_duration) ** 0.5)
- for interferometer in interferometers:
+ signal, interferometer, likelihood.waveform_generator.time_duration) ** 0.5)
+
+ for interferometer in likelihood.interferometers:
sample['{}_matched_filter_snr'.format(interferometer.name)] = matched_filter_snrs[interferometer.name]
sample['{}_optimal_snr'.format(interferometer.name)] = optimal_snrs[interferometer.name]
+
+ likelihood.interferometers = all_interferometers
+ print([interferometer.name for interferometer in likelihood.interferometers])
+
else:
logging.info('Not computing SNRs.')
--
GitLab