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| import time | ||
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| import jax | ||
| import jax.numpy as jnp | ||
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| from jimgw.jim import Jim | ||
| from jimgw.prior import CombinePrior, UniformPrior, UniformSpherePrior, CosinePrior, SinePrior, PowerLawPrior | ||
| from jimgw.single_event.detector import H1, L1 | ||
| from jimgw.single_event.likelihood import TransientLikelihoodFD | ||
| from jimgw.single_event.waveform import RippleIMRPhenomPv2 | ||
| from flowMC.strategy.optimization import optimization_Adam | ||
| from jimgw.single_event.transforms import SkyFrameToDetectorFrameSkyPositionTransform, SymmetricMassRatioToMassRatioTransform, SpinToCartesianSpinTransform, MassRatioToSymmetricMassRatioTransform | ||
| from jimgw.transforms import BoundToUnbound | ||
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| jax.config.update("jax_enable_x64", True) | ||
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| ########################################### | ||
| ########## First we grab data ############# | ||
| ########################################### | ||
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| total_time_start = time.time() | ||
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| # first, fetch a 4s segment centered on GW150914 | ||
| gps = 1126259462.4 | ||
| start = gps - 2 | ||
| end = gps + 2 | ||
| fmin = 20.0 | ||
| fmax = 1024.0 | ||
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| ifos = [H1, L1] | ||
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| H1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) | ||
| L1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) | ||
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| waveform = RippleIMRPhenomPv2(f_ref=20) | ||
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| ########################################### | ||
| ########## Set up priors ################## | ||
| ########################################### | ||
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| M_c_min, M_c_max = 10.0, 80.0 | ||
| eta_min, eta_max = 0.2, 0.25 | ||
| # m_1_prior = UniformPrior(Mc_q_to_m1_m2(M_c_min, q_max)[0], Mc_q_to_m1_m2(M_c_max, q_min)[0], parameter_names=["m_1"]) | ||
| # m_2_prior = UniformPrior(Mc_q_to_m1_m2(M_c_min, q_min)[1], Mc_q_to_m1_m2(M_c_max, q_max)[1], parameter_names=["m_2"]) | ||
| M_c_prior = UniformPrior(M_c_min, M_c_max, parameter_names=["M_c"]) | ||
| eta_prior = UniformPrior(eta_min, eta_max, parameter_names=["eta"]) | ||
| theta_jn_prior = SinePrior(parameter_names=["theta_jn"]) | ||
| phi_jl_prior = UniformPrior(0.0, 2 * jnp.pi, parameter_names=["phi_jl"]) | ||
| theta_1_prior = SinePrior(parameter_names=["theta_1"]) | ||
| theta_2_prior = SinePrior(parameter_names=["theta_2"]) | ||
| phi_12_prior = UniformPrior(0.0, 2 * jnp.pi, parameter_names=["phi_12"]) | ||
| a_1_prior = UniformPrior(0.0, 1.0, parameter_names=["a_1"]) | ||
| a_2_prior = UniformPrior(0.0, 1.0, parameter_names=["a_2"]) | ||
| dL_prior = PowerLawPrior(10.0, 2000.0, 2.0, parameter_names=["d_L"]) | ||
| t_c_prior = UniformPrior(-0.05, 0.05, parameter_names=["t_c"]) | ||
| phase_c_prior = UniformPrior(0.0, 2 * jnp.pi, parameter_names=["phase_c"]) | ||
| psi_prior = UniformPrior(0.0, jnp.pi, parameter_names=["psi"]) | ||
| ra_prior = UniformPrior(0.0, 2 * jnp.pi, parameter_names=["ra"]) | ||
| dec_prior = CosinePrior(parameter_names=["dec"]) | ||
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| prior = CombinePrior( | ||
| [ | ||
| # m_1_prior, | ||
| # m_2_prior, | ||
| M_c_prior, | ||
| eta_prior, | ||
| theta_jn_prior, | ||
| phi_jl_prior, | ||
| theta_1_prior, | ||
| theta_2_prior, | ||
| phi_12_prior, | ||
| a_1_prior, | ||
| a_2_prior, | ||
| dL_prior, | ||
| t_c_prior, | ||
| phase_c_prior, | ||
| psi_prior, | ||
| ra_prior, | ||
| dec_prior, | ||
| ] | ||
| ) | ||
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| sample_transforms = [ | ||
| # ComponentMassesToChirpMassMassRatioTransform, | ||
| BoundToUnbound(name_mapping = (["M_c"], ["M_c_unbounded"]), original_lower_bound=M_c_min, original_upper_bound=M_c_max), | ||
| BoundToUnbound(name_mapping = (["eta"], ["eta_unbounded"]), original_lower_bound=eta_min, original_upper_bound=eta_max), | ||
| BoundToUnbound(name_mapping = (["theta_jn"], ["theta_jn_unbounded"]) , original_lower_bound=0.0, original_upper_bound=jnp.pi), | ||
| BoundToUnbound(name_mapping = (["phi_jl"], ["phi_jl_unbounded"]) , original_lower_bound=0.0, original_upper_bound=2 * jnp.pi), | ||
| BoundToUnbound(name_mapping = (["theta_1"], ["theta_1_unbounded"]) , original_lower_bound=0.0, original_upper_bound=jnp.pi), | ||
| BoundToUnbound(name_mapping = (["theta_2"], ["theta_2_unbounded"]) , original_lower_bound=0.0, original_upper_bound=jnp.pi), | ||
| BoundToUnbound(name_mapping = (["phi_12"], ["phi_12_unbounded"]) , original_lower_bound=0.0, original_upper_bound=2 * jnp.pi), | ||
| BoundToUnbound(name_mapping = (["a_1"], ["a_1_unbounded"]) , original_lower_bound=0.0, original_upper_bound=1.0), | ||
| BoundToUnbound(name_mapping = (["a_2"], ["a_2_unbounded"]) , original_lower_bound=0.0, original_upper_bound=1.0), | ||
| BoundToUnbound(name_mapping = (["d_L"], ["d_L_unbounded"]) , original_lower_bound=10.0, original_upper_bound=2000.0), | ||
| BoundToUnbound(name_mapping = (["t_c"], ["t_c_unbounded"]) , original_lower_bound=-0.05, original_upper_bound=0.05), | ||
| BoundToUnbound(name_mapping = (["phase_c"], ["phase_c_unbounded"]) , original_lower_bound=0.0, original_upper_bound=2 * jnp.pi), | ||
| BoundToUnbound(name_mapping = (["psi"], ["psi_unbounded"]), original_lower_bound=0.0, original_upper_bound=jnp.pi), | ||
| SkyFrameToDetectorFrameSkyPositionTransform(gps_time=gps, ifos=ifos), | ||
| BoundToUnbound(name_mapping = (["zenith"], ["zenith_unbounded"]), original_lower_bound=0.0, original_upper_bound=jnp.pi), | ||
| BoundToUnbound(name_mapping = (["azimuth"], ["azimuth_unbounded"]), original_lower_bound=0.0, original_upper_bound=2 * jnp.pi), | ||
| ] | ||
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| likelihood_transforms = [ | ||
| # ComponentMassesToChirpMassMassRatioTransform, | ||
| SymmetricMassRatioToMassRatioTransform, | ||
| SpinToCartesianSpinTransform(freq_ref=20.0), | ||
| MassRatioToSymmetricMassRatioTransform, | ||
| ] | ||
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| likelihood = TransientLikelihoodFD( | ||
| ifos, waveform=waveform, trigger_time=gps, duration=4, post_trigger_duration=2 | ||
| ) | ||
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| mass_matrix = jnp.eye(prior.n_dim) | ||
| mass_matrix = mass_matrix.at[1, 1].set(1e-3) | ||
| mass_matrix = mass_matrix.at[9, 9].set(1e-3) | ||
| local_sampler_arg = {"step_size": mass_matrix * 1e-3} | ||
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| Adam_optimizer = optimization_Adam(n_steps=3000, learning_rate=0.01, noise_level=1) | ||
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| import optax | ||
| n_epochs = 20 | ||
| n_loop_training = 100 | ||
| total_epochs = n_epochs * n_loop_training | ||
| start = total_epochs//10 | ||
| learning_rate = optax.polynomial_schedule( | ||
| 1e-3, 1e-4, 4.0, total_epochs - start, transition_begin=start | ||
| ) | ||
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| jim = Jim( | ||
| likelihood, | ||
| prior, | ||
| sample_transforms=sample_transforms, | ||
| likelihood_transforms=likelihood_transforms, | ||
| n_loop_training=n_loop_training, | ||
| n_loop_production=20, | ||
| n_local_steps=10, | ||
| n_global_steps=1000, | ||
| n_chains=500, | ||
| n_epochs=n_epochs, | ||
| learning_rate=learning_rate, | ||
| n_max_examples=50000, | ||
| n_flow_sample=50000, | ||
| momentum=0.9, | ||
| batch_size=50000, | ||
| use_global=True, | ||
| keep_quantile=0.0, | ||
| train_thinning=1, | ||
| output_thinning=10, | ||
| local_sampler_arg=local_sampler_arg, | ||
| strategies=[Adam_optimizer,"default"], | ||
| ) | ||
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| jim.sample(jax.random.PRNGKey(42))#,initial_guess=chains) |
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