Commit 96129f0d authored by plasky's avatar plasky
Browse files

changed random seed to avoid multi-modal posteriors. Added comments to...

changed random seed to avoid multi-modal posteriors.  Added comments to tutorial so user can understand each step
parent a313d514
......@@ -9,15 +9,21 @@ from __future__ import division, print_function
import tupak
import numpy as np
# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
time_duration = 4.
sampling_frequency = 2048.
# Specify the output directory and the name of the simulation.
outdir = 'outdir'
label = 'basic_tutorial'
tupak.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
# We are going to inject a binary black hole waveform. We first establish a dictionary of parameters that
# includes all of the different waveform parameters, including masses of the two black holes (mass_1, mass_2),
# spins of both black holes (a, tilt, phi), etc.
injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, phi_12=1.7, phi_jl=0.3,
luminosity_distance=4000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375, dec=-1.2108)
......@@ -29,24 +35,33 @@ waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=ti
hf_signal = waveform_generator.frequency_domain_strain()
# Set up interferometers.
# Set up interferometers. In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
# and Virgo (V1)). These default to their design sensitivity
IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
name, injection_polarizations=hf_signal, injection_parameters=injection_parameters, time_duration=time_duration,
sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
# Set up prior
# Set up prior, which is a dictionary
priors = dict()
# These parameters will not be sampled
for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi', 'iota', 'ra', 'dec', 'geocent_time']:
# By default we will sample all terms in the signal models. However, this will take a long time for the calculation,
# so for this example we will set almost all of the priors to be equall to their injected values. This implies the
# prior is a delta function at the true, injected value. In reality, the sampler implementation is smart enough to
# not sample any parameter that has a delta-function prior.
for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi', 'ra', 'dec', 'geocent_time']:
priors[key] = injection_parameters[key]
# The above list does *not* include mass_1, mass_2, iota and luminosity_distance, which means those are the parameters
# that will be included in the sampler. If we do nothing, then the default priors get used.
priors['luminosity_distance'] = tupak.prior.create_default_prior(name='luminosity_distance')
# Initialise Likelihood
# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
likelihood = tupak.likelihood.Likelihood(interferometers=IFOs, waveform_generator=waveform_generator)
# Run sampler
# Run sampler. In this case we're going to use the `dynesty` sampler
result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
injection_parameters=injection_parameters, outdir=outdir, label=label)
# make some plots of the outputs
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