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Commit 617ab302 authored by Colm Talbot's avatar Colm Talbot
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3 merge requests!400Add Constraint prior,!377WIP: Reconstruct marginalised parameters - updated,!317Reconstruct marginalised parameters
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......@@ -2,6 +2,9 @@
"""
Tutorial to demonstrate how to improve the speed and efficiency of parameter
estimation on an injected signal using time, phase and distance marginalisation.
We also demonstrate how the posterior distribution for the marginalised
parameter can be recovered in post-processing.
"""
from __future__ import division, print_function
import bilby
......@@ -39,6 +42,10 @@ ifos.inject_signal(waveform_generator=waveform_generator,
# Set up prior
priors = bilby.gw.prior.BBHPriorDict()
priors['geocent_time'] = bilby.core.prior.Uniform(
minimum=injection_parameters['geocent_time'] - 1,
maximum=injection_parameters['geocent_time'] + 1,
name='geocent_time', latex_label='$t_c$', unit='$s$')
# These parameters will not be sampled
for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'iota', 'ra',
'dec']:
......@@ -46,7 +53,7 @@ for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'iota', 'ra',
# Initialise GravitationalWaveTransient
# Note that we now need to pass the: priors and flags for each thing that's
# being marginalised. A lookup table is used fro distance marginalisation which
# being marginalised. A lookup table is used for distance marginalisation which
# takes a few minutes to build.
likelihood = bilby.gw.GravitationalWaveTransient(
interferometers=ifos, waveform_generator=waveform_generator, priors=priors,
......@@ -54,7 +61,13 @@ likelihood = bilby.gw.GravitationalWaveTransient(
time_marginalization=True)
# Run sampler
# Note that we've added an additional argument `conversion_function`, this is
# a function that is applied to the posterior. Here it generates many additional
# parameters, e.g., source frame masses and effective spin parameters. It also
# reconstructs posterior distributions for the parameters which were
# marginalised over in the likelihood.
result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty',
injection_parameters=injection_parameters, outdir=outdir, label=label)
injection_parameters=injection_parameters, outdir=outdir, label=label,
conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
result.plot_corner()
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