update tutorial to use marginalised likelihood

tutorials/BasicTutorial.py

@@ -8,8 +8,10 @@ time_duration = 4.
 sampling_frequency = 2048.
 outdir = 'outdir'
 
-injection_parameters = dict(mass_1=36., mass_2=29., a_1=0, a_2=0, tilt_1=0, tilt_2=0, phi_12=0, phi_jl=0,
-                            luminosity_distance=2500., iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
+np.random.seed(170809)
+
+injection_parameters = dict(mass_1=36., mass_2=29., a_1=0.4, a_2=0, tilt_1=0, tilt_2=0, phi_12=0, phi_jl=0,
+                            luminosity_distance=8000., 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)
 
 # Create the waveform_generator using a LAL BinaryBlackHole source function
@@ -25,18 +27,24 @@ IFOs = [peyote.detector.get_inteferometer_with_fake_noise_and_injection(
     sampling_frequency=sampling_frequency, time_duration=time_duration, outdir=outdir)
     for name in ['H1', 'L1', 'V1']]
 
-# Set up likelihood
-likelihood = peyote.likelihood.Likelihood(IFOs, waveform_generator)
-
 # Set up prior
 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']:
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'ra', 'dec', 'phase', 'geocent_time',
+            'mass_1', 'mass_2']:
     priors[key] = injection_parameters[key]
+priors['luminosity_distance'] = peyote.prior.PowerLaw(alpha=2, minimum=500, maximum=10000, name='luminosity_distance')
+# priors['phase'] = peyote.prior.create_default_prior(name='phase')
+# priors['mass_1'] = peyote.prior.Uniform(30, 50, name='mass_1')
+
+# Set up likelihood
+likelihood = peyote.likelihood.MarginalizedLikelihood(IFOs, waveform_generator, prior=priors,
+                                                      distance_marginalization=False, phase_marginalization=False)
+# likelihood = peyote.likelihood.Likelihood(IFOs, waveform_generator)
 
 # Run sampler
 result = peyote.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty',
-                                    label='BasicTutorial', use_ratio=True, npoints=500, verbose=True,
+                                    label='BasicTutorial', use_ratio=True, npoints=200, verbose=True,
                                     injection_parameters=injection_parameters, outdir=outdir)
 result.plot_corner()
 result.plot_walks()