diff --git a/example/GW150914.py b/example/GW150914.py deleted file mode 100644 index 559b5b7c..00000000 --- a/example/GW150914.py +++ /dev/null @@ -1,138 +0,0 @@ -import time - -import jax -import jax.numpy as jnp - -from jimgw.jim import Jim -from jimgw.prior import Composite, Unconstrained_Uniform -from jimgw.single_event.detector import H1, L1 -from jimgw.single_event.likelihood import TransientLikelihoodFD -from jimgw.single_event.waveform import RippleIMRPhenomD -from flowMC.strategy.optimization import optimization_Adam - -jax.config.update("jax_enable_x64", True) - -########################################### -########## First we grab data ############# -########################################### - -total_time_start = time.time() - -# first, fetch a 4s segment centered on GW150914 -gps = 1126259462.4 -duration = 4 -post_trigger_duration = 2 -start_pad = duration - post_trigger_duration -end_pad = post_trigger_duration -fmin = 20.0 -fmax = 1024.0 - -ifos = ["H1", "L1"] - -H1.load_data(gps, start_pad, end_pad, fmin, fmax, psd_pad=16, tukey_alpha=0.2) -L1.load_data(gps, start_pad, end_pad, fmin, fmax, psd_pad=16, tukey_alpha=0.2) - -Mc_prior = Unconstrained_Uniform(10.0, 80.0, naming=["M_c"]) -q_prior = Unconstrained_Uniform( - 0.125, - 1.0, - naming=["q"], - transforms={"q": ("eta", lambda params: params["q"] / (1 + params["q"]) ** 2)}, -) -s1z_prior = Unconstrained_Uniform(-1.0, 1.0, naming=["s1_z"]) -s2z_prior = Unconstrained_Uniform(-1.0, 1.0, naming=["s2_z"]) -dL_prior = Unconstrained_Uniform(0.0, 2000.0, naming=["d_L"]) -t_c_prior = Unconstrained_Uniform(-0.05, 0.05, naming=["t_c"]) -phase_c_prior = Unconstrained_Uniform(0.0, 2 * jnp.pi, naming=["phase_c"]) -cos_iota_prior = Unconstrained_Uniform( - -1.0, - 1.0, - naming=["cos_iota"], - transforms={ - "cos_iota": ( - "iota", - lambda params: jnp.arccos( - jnp.arcsin(jnp.sin(params["cos_iota"] / 2 * jnp.pi)) * 2 / jnp.pi - ), - ) - }, -) -psi_prior = Unconstrained_Uniform(0.0, jnp.pi, naming=["psi"]) -ra_prior = Unconstrained_Uniform(0.0, 2 * jnp.pi, naming=["ra"]) -sin_dec_prior = Unconstrained_Uniform( - -1.0, - 1.0, - naming=["sin_dec"], - transforms={ - "sin_dec": ( - "dec", - lambda params: jnp.arcsin( - jnp.arcsin(jnp.sin(params["sin_dec"] / 2 * jnp.pi)) * 2 / jnp.pi - ), - ) - }, -) - -prior = Composite( - [ - Mc_prior, - q_prior, - s1z_prior, - s2z_prior, - dL_prior, - t_c_prior, - phase_c_prior, - cos_iota_prior, - psi_prior, - ra_prior, - sin_dec_prior, - ] -) -likelihood = TransientLikelihoodFD( - [H1, L1], - waveform=RippleIMRPhenomD(), - trigger_time=gps, - duration=4, - post_trigger_duration=2, -) - - -mass_matrix = jnp.eye(11) -mass_matrix = mass_matrix.at[1, 1].set(1e-3) -mass_matrix = mass_matrix.at[5, 5].set(1e-3) -local_sampler_arg = {"step_size": mass_matrix * 3e-3} - -Adam_optimizer = optimization_Adam(n_steps=3000, learning_rate=0.01, noise_level=1) - -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 -) - - -jim = Jim( - likelihood, - prior, - 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_samples=100000, - momentum=0.9, - batch_size=30000, - use_global=True, - train_thinning=1, - output_thinning=10, - local_sampler_arg=local_sampler_arg, - strategies=[Adam_optimizer,"default"], -) - -jim.sample(jax.random.PRNGKey(42)) diff --git a/example/GW150914_IMRPhenomD.py b/example/GW150914_IMRPhenomD.py new file mode 100644 index 00000000..7a7a37b3 --- /dev/null +++ b/example/GW150914_IMRPhenomD.py @@ -0,0 +1,133 @@ +import jax +import jax.numpy as jnp + +from jimgw.jim import Jim +from jimgw.prior import CombinePrior, UniformPrior, CosinePrior, SinePrior, PowerLawPrior +from jimgw.single_event.detector import H1, L1 +from jimgw.single_event.likelihood import TransientLikelihoodFD +from jimgw.single_event.waveform import RippleIMRPhenomD +from jimgw.transforms import BoundToUnbound +from jimgw.single_event.transforms import ComponentMassesToChirpMassSymmetricMassRatioTransform, SkyFrameToDetectorFrameSkyPositionTransform, ComponentMassesToChirpMassMassRatioTransform +from jimgw.single_event.utils import Mc_q_to_m1_m2 +from flowMC.strategy.optimization import optimization_Adam + +jax.config.update("jax_enable_x64", True) + +########################################### +########## First we grab data ############# +########################################### + +# first, fetch a 4s segment centered on GW150914 +gps = 1126259462.4 +duration = 4 +post_trigger_duration = 2 +start_pad = duration - post_trigger_duration +end_pad = post_trigger_duration +fmin = 20.0 +fmax = 1024.0 + +ifos = [H1, L1] + +for ifo in ifos: + ifo.load_data(gps, start_pad, end_pad, fmin, fmax, psd_pad=16, tukey_alpha=0.2) + +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"]) +Mc_prior = UniformPrior(M_c_min, M_c_max, parameter_names=["M_c"]) +eta_prior = UniformPrior(eta_min, eta_max, parameter_names=["eta"]) +s1z_prior = UniformPrior(-1.0, 1.0, parameter_names=["s1_z"]) +s2z_prior = UniformPrior(-1.0, 1.0, parameter_names=["s2_z"]) +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"]) +iota_prior = SinePrior(parameter_names=["iota"]) +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"]) + +prior = CombinePrior( + [ + Mc_prior, + eta_prior, + s1z_prior, + s2z_prior, + dL_prior, + t_c_prior, + phase_c_prior, + iota_prior, + psi_prior, + ra_prior, + dec_prior, + ] +) + +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 = (["s1_z"], ["s1_z_unbounded"]) , original_lower_bound=-1.0, original_upper_bound=1.0), + BoundToUnbound(name_mapping = (["s2_z"], ["s2_z_unbounded"]) , original_lower_bound=-1.0, original_upper_bound=1.0), + BoundToUnbound(name_mapping = (["d_L"], ["d_L_unbounded"]) , original_lower_bound=1.0, 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 = (["iota"], ["iota_unbounded"]), original_lower_bound=0., original_upper_bound=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), +] + +likelihood_transforms = [ + # ComponentMassesToChirpMassSymmetricMassRatioTransform, +] + +likelihood = TransientLikelihoodFD( + ifos, + waveform=RippleIMRPhenomD(), + trigger_time=gps, + duration=4, + post_trigger_duration=2, +) + + +mass_matrix = jnp.eye(11) +mass_matrix = mass_matrix.at[1, 1].set(1e-3) +mass_matrix = mass_matrix.at[5, 5].set(1e-3) +local_sampler_arg = {"step_size": mass_matrix * 3e-3} + +Adam_optimizer = optimization_Adam(n_steps=3000, learning_rate=0.01, noise_level=1) + +n_epochs = 30 +n_loop_training = 20 +learning_rate = 1e-4 + + +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_samples=100000, + momentum=0.9, + batch_size=30000, + use_global=True, + train_thinning=1, + output_thinning=10, + local_sampler_arg=local_sampler_arg, + strategies=[Adam_optimizer, "default"], + verbose=True +) + +jim.sample(jax.random.PRNGKey(42)) +# jim.get_samples() +# jim.print_summary() \ No newline at end of file diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index fae0bc98..8e6bb0bc 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -104,18 +104,19 @@ def posterior(self, params: Float[Array, " n_dim"], data: dict): def sample(self, key: PRNGKeyArray, initial_position: Array = jnp.array([])): if initial_position.size == 0: - initial_guess = [] - for _ in range(self.sampler.n_chains): - flag = True - while flag: - key = jax.random.split(key)[1] - guess = self.prior.sample(key, 1) - for transform in self.sample_transforms: - guess = transform.forward(guess) - guess = jnp.array([i for i in guess.values()]).T[0] - flag = not jnp.all(jnp.isfinite(guess)) - initial_guess.append(guess) - initial_position = jnp.array(initial_guess) + initial_position = jnp.zeros((self.sampler.n_chains, self.prior.n_dim)) + jnp.nan + + while not jax.tree.reduce(jnp.logical_and, jax.tree.map(lambda x: jnp.isfinite(x), initial_position)).all(): + non_finite_index = jnp.any(~jax.tree.reduce(jnp.logical_and, jax.tree.map(lambda x: jnp.isfinite(x), initial_position)),axis=1) + + key, subkey = jax.random.split(key) + guess = self.prior.sample(subkey, self.sampler.n_chains) + for transform in self.sample_transforms: + guess = jax.vmap(transform.forward)(guess) + guess = jnp.array(jax.tree.leaves({key: guess[key] for key in self.parameter_names})).T + finite_guess = jnp.where(jnp.all(jax.tree.map(lambda x: jnp.isfinite(x), guess),axis=1))[0] + common_length = min(len(finite_guess), len(non_finite_index)) + initial_position = initial_position.at[non_finite_index[:common_length]].set(guess[:common_length]) self.sampler.sample(initial_position, None) # type: ignore def maximize_likelihood(