created full 15 dimensional parameter space example

examples/injection_examples/standard_15d_cbc_tutorial.py

+#!/usr/bin/env python
+"""
+Tutorial to demonstrate running parameter estimation on a full 15 parameter
+space for an injected cbc signal. This is the standard injection analysis script
+one can modify for the study of injeted CBC events. 
+"""
+from __future__ import division, print_function
+
+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
+duration = 4.
+sampling_frequency = 2048.
+
+# Specify the output directory and the name of the simulation.
+outdir = 'outdir'
+label = 'full_15_parameters'
+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),
+# 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=200., 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',
+                          reference_frequency=50., minimum_frequency=20.)
+
+# Create the waveform_generator using a LAL BinaryBlackHole source function
+# the generator will convert all the parameters
+waveform_generator = bilby.gw.WaveformGenerator(
+    duration=duration, sampling_frequency=sampling_frequency,
+    frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
+    parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,
+    waveform_arguments=waveform_arguments)
+
+# Set up interferometers.  In this case we'll use two interferometers
+# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design
+# sensitivity
+ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
+ifos.set_strain_data_from_power_spectral_densities(
+    sampling_frequency=sampling_frequency, duration=duration,
+    start_time=injection_parameters['geocent_time'] - 3)
+ifos.inject_signal(waveform_generator=waveform_generator,
+                   parameters=injection_parameters)
+
+
+# For this analysis, we implemenet the standard BBH priors defined, except for
+# the definition of the time prior, which is defined as uniform about the
+# injected value.
+# Furthermore, we decide to sample in chirp mass and mass ratio, due to the
+# preferred shape for the associated posterior distributions.
+priors = bilby.gw.prior.BBHPriorDict()
+priors.pop('mass_1')
+priors.pop('mass_2')
+
+priors['chirp_mass'] = bilby.prior.Uniform(
+    name='chirp_mass', latex_label='$M$', minimum=10.0, maximum=100.0,
+    unit='$M_{\\odot}$')
+
+priors['mass_ratio'] = bilby.prior.Uniform(
+    name='mass_ratio', latex_label='$q$', minimum=0.5, maximum=1.0)
+
+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='$s$')
+
+# Initialise the likelihood by passing in the interferometer data (ifos) and
+# the waveoform generator, as well the priors.
+# The explicit time, distance, and phase marginalizations are turned on to
+# improve convergence, and the parameters are recovered by the conversion
+# function.
+likelihood = bilby.gw.GravitationalWaveTransient(
+    interferometers=ifos, waveform_generator=waveform_generator,prior=priors,
+    distance_marginalization=True, phase_marginalization=True, time_marginalization=True)
+
+# Run sampler.  In this case we're going to use the `cpnest` sampler
+# Note that the maxmcmc parameter is increased so that between each iteration of
+# the nested sampler approach, the walkers will move further using an mcmc
+# approach, searching the full parameter space.
+# The conversion function will determine the distance, phase and coalescence
+# time posteriors in post processing.
+result = bilby.run_sampler(
+    likelihood=likelihood, priors=priors, sampler='cpnest', npoints=2000,
+    injection_parameters=injection_parameters, outdir=outdir,
+    label=label, maxmcmc=2000,
+    conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
+
+# Make a corner plot.
+result.plot_corner()