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Merge pull request #163 from kazewong/Fix-spin-reparameterization
Fix spin reparameterization
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| Original file line number | Diff line number | Diff line change |
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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.jim import Jim | ||
| from jimgw.prior import ( | ||
| CombinePrior, | ||
| UniformPrior, | ||
| CosinePrior, | ||
| SinePrior, | ||
| PowerLawPrior, | ||
| UniformSpherePrior, | ||
| ) | ||
| from jimgw.single_event.detector import H1, L1 | ||
| from jimgw.single_event.likelihood import TransientLikelihoodFD | ||
| from jimgw.single_event.waveform import RippleIMRPhenomPv2 | ||
| from jimgw.transforms import BoundToUnbound | ||
| from jimgw.single_event.transforms import ( | ||
| SkyFrameToDetectorFrameSkyPositionTransform, | ||
| SphereSpinToCartesianSpinTransform, | ||
| MassRatioToSymmetricMassRatioTransform, | ||
| DistanceToSNRWeightedDistanceTransform, | ||
| GeocentricArrivalTimeToDetectorArrivalTimeTransform, | ||
| GeocentricArrivalPhaseToDetectorArrivalPhaseTransform, | ||
| ) | ||
| from jimgw.single_event.utils import Mc_q_to_m1_m2 | ||
| from flowMC.strategy.optimization import optimization_Adam | ||
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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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| prior = [] | ||
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| # Mass prior | ||
| M_c_min, M_c_max = 10.0, 80.0 | ||
| q_min, q_max = 0.125, 1.0 | ||
| Mc_prior = UniformPrior(M_c_min, M_c_max, parameter_names=["M_c"]) | ||
| q_prior = UniformPrior(q_min, q_max, parameter_names=["q"]) | ||
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| prior = prior + [Mc_prior, q_prior] | ||
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| # Spin prior | ||
| s1_prior = UniformSpherePrior(parameter_names=["s1"]) | ||
| s2_prior = UniformSpherePrior(parameter_names=["s2"]) | ||
| iota_prior = SinePrior(parameter_names=["iota"]) | ||
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| prior = prior + [ | ||
| s1_prior, | ||
| s2_prior, | ||
| iota_prior, | ||
| ] | ||
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| # Extrinsic prior | ||
| dL_prior = PowerLawPrior(1.0, 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 = prior + [ | ||
| dL_prior, | ||
| t_c_prior, | ||
| phase_c_prior, | ||
| psi_prior, | ||
| ra_prior, | ||
| dec_prior, | ||
| ] | ||
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| prior = CombinePrior(prior) | ||
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| # Defining Transforms | ||
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| sample_transforms = [ | ||
| DistanceToSNRWeightedDistanceTransform(gps_time=gps, ifos=ifos, dL_min=dL_prior.xmin, dL_max=dL_prior.xmax), | ||
| GeocentricArrivalPhaseToDetectorArrivalPhaseTransform(gps_time=gps, ifo=ifos[0]), | ||
| GeocentricArrivalTimeToDetectorArrivalTimeTransform(tc_min=t_c_prior.xmin, tc_max=t_c_prior.xmax, gps_time=gps, ifo=ifos[0]), | ||
| SkyFrameToDetectorFrameSkyPositionTransform(gps_time=gps, ifos=ifos), | ||
| BoundToUnbound(name_mapping = (["M_c"], ["M_c_unbounded"]), original_lower_bound=M_c_min, original_upper_bound=M_c_max), | ||
| BoundToUnbound(name_mapping = (["q"], ["q_unbounded"]), original_lower_bound=q_min, original_upper_bound=q_max), | ||
| BoundToUnbound(name_mapping = (["s1_phi"], ["s1_phi_unbounded"]) , original_lower_bound=0.0, original_upper_bound=2 * jnp.pi), | ||
| BoundToUnbound(name_mapping = (["s2_phi"], ["s2_phi_unbounded"]) , original_lower_bound=0.0, original_upper_bound=2 * jnp.pi), | ||
| BoundToUnbound(name_mapping = (["iota"], ["iota_unbounded"]) , original_lower_bound=0.0, original_upper_bound=jnp.pi), | ||
| BoundToUnbound(name_mapping = (["s1_theta"], ["s1_theta_unbounded"]) , original_lower_bound=0.0, original_upper_bound=jnp.pi), | ||
| BoundToUnbound(name_mapping = (["s2_theta"], ["s2_theta_unbounded"]) , original_lower_bound=0.0, original_upper_bound=jnp.pi), | ||
| BoundToUnbound(name_mapping = (["s1_mag"], ["s1_mag_unbounded"]) , original_lower_bound=0.0, original_upper_bound=0.99), | ||
| BoundToUnbound(name_mapping = (["s2_mag"], ["s2_mag_unbounded"]) , original_lower_bound=0.0, original_upper_bound=0.99), | ||
| BoundToUnbound(name_mapping = (["phase_det"], ["phase_det_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), | ||
| 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 = [ | ||
| MassRatioToSymmetricMassRatioTransform, | ||
| SphereSpinToCartesianSpinTransform("s1"), | ||
| SphereSpinToCartesianSpinTransform("s2"), | ||
| ] | ||
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| likelihood = TransientLikelihoodFD( | ||
| [H1, L1], 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 | ||
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| 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=30000, | ||
| n_flow_sample=100000, | ||
| momentum=0.9, | ||
| batch_size=30000, | ||
| 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)) |
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