Delete Some Files (09bb24a9) · Commits · Wei Changfeng / BBH Mimicking

2021_06/test_one_BBH_in_Bilby_and_VItamin/fast_tutorial.py

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		#!/usr/bin/env python
		"""
		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 Planck15.
		"""

		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 = 1.
		sampling_frequency = 256.

		# Specify the output directory and the name of the simulation.
		outdir = 'outdir'
		label = 'bbh_in_bilby_and_vitamin_0605'
		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=86., mass_2=69., 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., theta_jn=0.4, psi=2.659,
		phase=1.3, geocent_time=1126259642.5+0.22, 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
		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', 'V1'])
		ifos.set_strain_data_from_power_spectral_densities(
		sampling_frequency=sampling_frequency, duration=duration,
		start_time=injection_parameters['geocent_time'] - 0.5)
		ifos.inject_signal(waveform_generator=waveform_generator,
		parameters=injection_parameters)

		# Set up a PriorDict, 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 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, theta_jn 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.BBHPriorDict()
		priors.pop('chirp_mass')
		priors.pop('mass_ratio')

		priors['mass_1'] = bilby.core.prior.Uniform(name='mass_1', minimum=30, maximum=160, unit='$M_{\\odot}$')
		priors['mass_2'] = bilby.core.prior.Uniform(name='mass_2', minimum=30, maximum=160, unit='$M_{\\odot}$')
		priors['luminosity_distance'] = bilby.prior.Uniform(minimum=1000, maximum=3000, name='luminosity_distance', latex_label='luminosity_distance')


		priors['geocent_time'] = bilby.core.prior.Uniform(
		minimum=injection_parameters['geocent_time'] + 0.15,
		maximum=injection_parameters['geocent_time'] + 0.35,
		name='geocent_time', latex_label='$t_c$', unit='$s$')
		for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl',
		'phase']:
		priors[key] = injection_parameters[key]

		# Initialise the likelihood by passing in the interferometer data (ifos) and
		# the waveform generator
		likelihood = bilby.gw.GravitationalWaveTransient(
		interferometers=ifos, waveform_generator=waveform_generator)

		# Run sampler. In this case we're going to use the `dynesty` sampler
		result = bilby.run_sampler(
		likelihood=likelihood, priors=priors, sampler='dynesty', npoints=2000,
		injection_parameters=injection_parameters, outdir=outdir, label=label)

		# Make a corner plot.
		result.plot_corner()

2021_06/test_one_BBH_in_Bilby_and_VItamin/fast_tutorial_15p.py

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		#!/usr/bin/env python
		"""
		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 Planck15.
		"""

		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 = 1.
		sampling_frequency = 256.

		# Specify the output directory and the name of the simulation.
		outdir = 'outdir_15p'
		label = 'bbh_15p_in_bilby_and_vitamin_0605'
		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=86., mass_2=69., 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., theta_jn=0.4, psi=2.659,
		phase=1.3, geocent_time=1126259642.5+0.22, 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
		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', 'V1'])
		ifos.set_strain_data_from_power_spectral_densities(
		sampling_frequency=sampling_frequency, duration=duration,
		start_time=injection_parameters['geocent_time'] - 0.5)
		ifos.inject_signal(waveform_generator=waveform_generator,
		parameters=injection_parameters)

		# Set up a PriorDict, 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 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, theta_jn 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.BBHPriorDict()
		priors.pop('chirp_mass')
		priors.pop('mass_ratio')

		priors['mass_1'] = bilby.core.prior.Uniform(name='mass_1', minimum=30, maximum=160, unit='$M_{\\odot}$')
		priors['mass_2'] = bilby.core.prior.Uniform(name='mass_2', minimum=30, maximum=160, unit='$M_{\\odot}$')
		priors['luminosity_distance'] = bilby.prior.Uniform(minimum=1000, maximum=3000, name='luminosity_distance', latex_label='luminosity_distance')


		priors['geocent_time'] = bilby.core.prior.Uniform(
		minimum=injection_parameters['geocent_time'] + 0.15,
		maximum=injection_parameters['geocent_time'] + 0.35,
		name='geocent_time', latex_label='$t_c$', unit='$s$')


		# Initialise the likelihood by passing in the interferometer data (ifos) and
		# the waveform generator
		likelihood = bilby.gw.GravitationalWaveTransient(
		interferometers=ifos, waveform_generator=waveform_generator)

		# Run sampler. In this case we're going to use the `dynesty` sampler
		result = bilby.run_sampler(
		likelihood=likelihood, priors=priors, sampler='dynesty', npoints=2000,
		injection_parameters=injection_parameters, outdir=outdir, label=label)

		# Make a corner plot.
		result.plot_corner()

2021_06/test_one_BBH_in_Bilby_and_VItamin/outdir/JSD_calculation.ipynb

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2021_06/test_one_BBH_in_Bilby_and_VItamin/outdir/bbh_in_bilby_and_vitamin_0605.log

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Original line number	Diff line number	Diff line
		11:21 INFO : Waveform generator initiated with
		frequency_domain_source_model: bilby.gw.source.lal_binary_black_hole
		time_domain_source_model: None
		parameter_conversion: bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters
		11:21 INFO : Injected signal in H1:
		11:21 INFO : optimal SNR = 22.66
		11:21 INFO : matched filter SNR = 23.25+1.54j
		11:21 INFO : mass_1 = 86.0
		11:21 INFO : mass_2 = 69.0
		11:21 INFO : a_1 = 0.4
		11:21 INFO : a_2 = 0.3
		11:21 INFO : tilt_1 = 0.5
		11:21 INFO : tilt_2 = 1.0
		11:21 INFO : phi_12 = 1.7
		11:21 INFO : phi_jl = 0.3
		11:21 INFO : luminosity_distance = 2000.0
		11:21 INFO : theta_jn = 0.4
		11:21 INFO : psi = 2.659
		11:21 INFO : phase = 1.3
		11:21 INFO : geocent_time = 1126259642.72
		11:21 INFO : ra = 1.375
		11:21 INFO : dec = -1.2108
		11:21 INFO : Injected signal in L1:
		11:21 INFO : optimal SNR = 18.35
		11:21 INFO : matched filter SNR = 17.41-0.83j
		11:21 INFO : mass_1 = 86.0
		11:21 INFO : mass_2 = 69.0
		11:21 INFO : a_1 = 0.4
		11:21 INFO : a_2 = 0.3
		11:21 INFO : tilt_1 = 0.5
		11:21 INFO : tilt_2 = 1.0
		11:21 INFO : phi_12 = 1.7
		11:21 INFO : phi_jl = 0.3
		11:21 INFO : luminosity_distance = 2000.0
		11:21 INFO : theta_jn = 0.4
		11:21 INFO : psi = 2.659
		11:21 INFO : phase = 1.3
		11:21 INFO : geocent_time = 1126259642.72
		11:21 INFO : ra = 1.375
		11:21 INFO : dec = -1.2108
		11:21 INFO : Injected signal in V1:
		11:21 INFO : optimal SNR = 18.71
		11:21 INFO : matched filter SNR = 16.50-0.21j
		11:21 INFO : mass_1 = 86.0
		11:21 INFO : mass_2 = 69.0
		11:21 INFO : a_1 = 0.4
		11:21 INFO : a_2 = 0.3
		11:21 INFO : tilt_1 = 0.5
		11:21 INFO : tilt_2 = 1.0
		11:21 INFO : phi_12 = 1.7
		11:21 INFO : phi_jl = 0.3
		11:21 INFO : luminosity_distance = 2000.0
		11:21 INFO : theta_jn = 0.4
		11:21 INFO : psi = 2.659
		11:21 INFO : phase = 1.3
		11:21 INFO : geocent_time = 1126259642.72
		11:21 INFO : ra = 1.375
		11:21 INFO : dec = -1.2108
		11:21 INFO : No prior given, using default BBH priors in /home/weichangfeng/.local/lib/python3.8/site-packages/bilby/gw/prior_files/precessing_spins_bbh.prior.
		11:21 INFO : Running for label 'bbh_in_bilby_and_vitamin_0605', output will be saved to 'outdir'
		11:21 INFO : Using lal version 7.1.0
		11:21 INFO : Using lal git version Branch: None;Tag: lalsuite-v6.79;Id: 2439365b5a320735efaf7171b37738032dcf7b6b;;Builder: Unknown User <>;Repository status: UNCLEAN: Modified working tree
		11:21 INFO : Using lalsimulation version 2.3.0
		11:21 INFO : Using lalsimulation git version Branch: None;Tag: lalsuite-v6.79;Id: 2439365b5a320735efaf7171b37738032dcf7b6b;;Builder: Unknown User <>;Repository status: UNCLEAN: Modified working tree
		11:21 INFO : Search parameters:
		11:21 INFO : mass_1 = Uniform(minimum=30, maximum=160, name='mass_1', latex_label='$m_1$', unit='$M_{\\odot}$', boundary=None)
		11:21 INFO : mass_2 = Uniform(minimum=30, maximum=160, name='mass_2', latex_label='$m_2$', unit='$M_{\\odot}$', boundary=None)
		11:21 INFO : luminosity_distance = Uniform(minimum=1000, maximum=3000, name='luminosity_distance', latex_label='luminosity_distance', unit=None, boundary=None)
		11:21 INFO : dec = Cosine(name='dec', latex_label='$\\mathrm{DEC}$', unit=None, minimum=-1.5707963267948966, maximum=1.5707963267948966, boundary=None)
		11:21 INFO : ra = Uniform(minimum=0, maximum=6.283185307179586, name='ra', latex_label='$\\mathrm{RA}$', unit=None, boundary='periodic')
		11:21 INFO : theta_jn = Sine(name='theta_jn', latex_label='$\\theta_{JN}$', unit=None, minimum=0, maximum=3.141592653589793, boundary=None)
		11:21 INFO : psi = Uniform(minimum=0, maximum=3.141592653589793, name='psi', latex_label='$\\psi$', unit=None, boundary='periodic')
		11:21 INFO : geocent_time = Uniform(minimum=1126259642.8700001, maximum=1126259643.07, name='geocent_time', latex_label='$t_c$', unit='$s$', boundary=None)
		11:21 INFO : phase = 1.3
		11:21 INFO : a_1 = 0.4
		11:21 INFO : a_2 = 0.3
		11:21 INFO : tilt_1 = 0.5
		11:21 INFO : tilt_2 = 1.0
		11:21 INFO : phi_12 = 1.7
		11:21 INFO : phi_jl = 0.3
		11:21 INFO : Single likelihood evaluation took 9.233e-04 s
		11:21 INFO : Using sampler Dynesty with kwargs {'bound': 'multi', 'sample': 'rwalk', 'verbose': True, 'periodic': None, 'reflective': None, 'check_point_delta_t': 600, 'nlive': 2000, 'first_update': None, 'walks': 100, 'npdim': None, 'rstate': None, 'queue_size': 1, 'pool': None, 'use_pool': None, 'live_points': None, 'logl_args': None, 'logl_kwargs': None, 'ptform_args': None, 'ptform_kwargs': None, 'enlarge': 1.5, 'bootstrap': None, 'vol_dec': 0.5, 'vol_check': 8.0, 'facc': 0.2, 'slices': 5, 'update_interval': 1200, 'print_func': <bound method Dynesty._print_func of <bilby.core.sampler.dynesty.Dynesty object at 0x7f61b8e5a2e0>>, 'dlogz': 0.1, 'maxiter': None, 'maxcall': None, 'logl_max': inf, 'add_live': True, 'print_progress': True, 'save_bounds': False, 'n_effective': None, 'maxmcmc': 5000, 'nact': 5}
		11:21 INFO : Checkpoint every check_point_delta_t = 600s
		11:21 INFO : Using dynesty version 1.0.1
		11:21 INFO : Using the bilby-implemented rwalk sample method with ACT estimated walks
		11:21 INFO : Resume file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle does not exist.
		11:21 INFO : Generating initial points from the prior
		11:31 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		11:41 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		16:07 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		16:17 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		16:28 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		16:38 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		16:48 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		16:59 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		17:09 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		17:19 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		17:30 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		17:40 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		17:51 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		18:01 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		18:12 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		18:22 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		18:33 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		18:43 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		18:43 INFO : Writing 106 current samples to outdir/bbh_in_bilby_and_vitamin_0605_samples.dat
		18:54 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		18:54 INFO : Writing 183 current samples to outdir/bbh_in_bilby_and_vitamin_0605_samples.dat
		19:05 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		19:05 INFO : Writing 313 current samples to outdir/bbh_in_bilby_and_vitamin_0605_samples.dat
		19:15 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		19:15 INFO : Writing 524 current samples to outdir/bbh_in_bilby_and_vitamin_0605_samples.dat
		19:26 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		19:26 INFO : Writing 926 current samples to outdir/bbh_in_bilby_and_vitamin_0605_samples.dat
		19:37 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		19:37 INFO : Writing 1197 current samples to outdir/bbh_in_bilby_and_vitamin_0605_samples.dat
		19:47 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		19:47 INFO : Writing 2706 current samples to outdir/bbh_in_bilby_and_vitamin_0605_samples.dat
		19:58 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		19:58 INFO : Writing 5969 current samples to outdir/bbh_in_bilby_and_vitamin_0605_samples.dat
		20:09 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		20:09 INFO : Writing 11780 current samples to outdir/bbh_in_bilby_and_vitamin_0605_samples.dat
		20:15 INFO : Written checkpoint file outdir/bbh_in_bilby_and_vitamin_0605_resume.pickle
		20:15 INFO : Writing 11814 current samples to outdir/bbh_in_bilby_and_vitamin_0605_samples.dat
		20:17 INFO : Sampling time: 8:53:32.591021
		20:17 INFO : Summary of results:
		nsamples: 52514
		ln_noise_evidence: -857.034
		ln_evidence: -854.148 +/- 0.138
		ln_bayes_factor: 2.886 +/- 0.138

2021_06/test_one_BBH_in_Bilby_and_VItamin/outdir/bbh_in_bilby_and_vitamin_0605_checkpoint_run.png

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