diff --git a/examples/injection_examples/basic_tutorial.py b/examples/injection_examples/basic_tutorial.py
index 90202b5f492755e2f4b23f18e0b7760e857e1807..f4b5011a53bfa5d7035d1e5816dcd81a1b08fd95 100644
--- a/examples/injection_examples/basic_tutorial.py
+++ b/examples/injection_examples/basic_tutorial.py
@@ -1,9 +1,12 @@
#!/bin/python
"""
-Tutorial to demonstrate running parameter estimation on a reduced parameter space for an injected signal.
+Tutorial to demonstrate running parameter estimation on a reduced parameter
+space for an injected signal.
-This example estimates the masses using a uniform prior in both component masses and distance using a uniform in
-comoving volume prior on luminosity distance between luminosity distances of 100Mpc and 5Gpc, the cosmology is WMAP7.
+This example estimates the masses using a uniform prior in both component masses
+and distance using a uniform in
+comoving volume prior on luminosity distance between luminosity distances of
+100Mpc and 5Gpc, the cosmology is WMAP7.
"""
from __future__ import division, print_function
@@ -11,7 +14,8 @@ import numpy as np
import bilby
-# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
+# Set the duration and sampling frequency of the data segment that we're going
+# to inject the signal into
duration = 4.
sampling_frequency = 2048.
@@ -24,12 +28,14 @@ bilby.core.utils.setup_logger(outdir=outdir, label=label)
# Set up a random seed for result reproducibility. This is optional!
np.random.seed(88170235)
-# 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),
+# 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=2000., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
- ra=1.375, dec=-1.2108)
+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, ra=1.375, dec=-1.2108)
# Fixed arguments passed into the source model
waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
@@ -51,29 +57,37 @@ ifos.set_strain_data_from_power_spectral_densities(
ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
-# Set up prior, which is a dictionary
-# 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.
-# 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.
+# Set up PriorSet, which inherits from dict
+# 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 equal 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. 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 = bilby.gw.prior.BBHPriorSet()
-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='$s$')
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra', 'dec', 'geocent_time', 'phase']:
+# 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='$s$')
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'psi', 'ra',
+ 'dec', 'geocent_time', 'phase']:
priors[key] = injection_parameters[key]
-# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
-likelihood = bilby.gw.GravitationalWaveTransient(interferometers=ifos, waveform_generator=waveform_generator,
- time_marginalization=False, phase_marginalization=False,
- distance_marginalization=False, prior=priors)
+# Initialise the likelihood by passing in the interferometer data (IFOs)
+# and the waveoform generator
+likelihood = bilby.gw.GravitationalWaveTransient(
+ interferometers=ifos, waveform_generator=waveform_generator,
+ time_marginalization=False, phase_marginalization=False,
+ distance_marginalization=False, prior=priors)
# Run sampler. In this case we're going to use the `dynesty` sampler
-result = bilby.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
- injection_parameters=injection_parameters, outdir=outdir, label=label)
+result = bilby.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='nestle', npoints=1000,
+ injection_parameters=injection_parameters, outdir=outdir, label=label,
+ conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
+
# make some plots of the outputs
result.plot_corner()