Added GW150914_IMRPhenomPv2.py

example/GW150914_IMRPhenomPv2.py

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+import time
+
+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 RippleIMRPhenomPv2
+from jimgw.transforms import BoundToUnbound
+from jimgw.single_event.transforms import MassRatioToSymmetricMassRatioTransform, SpinToCartesianSpinTransform, ComponentMassesToChirpMassMassRatioTransform, SkyFrameToDetectorFrameSkyPositionTransform
+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 #############
+###########################################
+
+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]
+
+f_ref = 20.0
+
+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
+q_min, q_max = 0.125, 1.0
+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"])
+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"])
+
+prior = CombinePrior(
+    [
+        m_1_prior,
+        m_2_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,
+    ]
+)
+
+sample_transforms = [
+    ComponentMassesToChirpMassMassRatioTransform,
+    BoundToUnbound(name_mapping = [["M_c"], ["M_c_unbounded"]], original_lower_bound=10.0, original_upper_bound=80.0),
+    BoundToUnbound(name_mapping = [["q"], ["q_unbounded"]], original_lower_bound=0.125, original_upper_bound=1.),
+    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),
+]
+
+likelihood_transforms = [
+    ComponentMassesToChirpMassMassRatioTransform,
+    SpinToCartesianSpinTransform(freq_ref=f_ref),
+    MassRatioToSymmetricMassRatioTransform,
+]
+
+likelihood = TransientLikelihoodFD(
+    ifos,
+    waveform=RippleIMRPhenomPv2(f_ref=f_ref),
+    trigger_time=gps,
+    duration=4,
+    post_trigger_duration=2,
+)
+
+
+mass_matrix = jnp.eye(15)
+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}
+
+Adam_optimizer = optimization_Adam(n_steps=3000, learning_rate=0.01, noise_level=1)
+
+n_epochs = 30
+n_loop_training = 100
+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"],
+)
+
+jim.sample(jax.random.PRNGKey(42))
+jim.get_samples()
+jim.print_summary()
+
+###########################################
+########## Visualize the Data #############
+###########################################
+# import corner
+# import matplotlib.pyplot as plt
+# import numpy as np
+
+# production_summary = jim.sampler.get_sampler_state(training=False)
+# production_chain = production_summary["chains"].reshape(-1, len(jim.parameter_names)).T
+# if jim.sample_transforms:
+#     transformed_chain = jim.add_name(production_chain)
+#     for transform in reversed(jim.sample_transforms):
+#         transformed_chain = transform.backward(transformed_chain)
+# result = transformed_chain
+# labels = list(transformed_chain.keys())
+
+# samples = np.array(list(result.values())).reshape(int(len(labels)), -1) # flatten the array
+# transposed_array = samples.T # transpose the array
+# figure = corner.corner(transposed_array, labels=labels, plot_datapoints=False, title_quantiles=[0.16, 0.5, 0.84], show_titles=True, title_fmt='g', use_math_text=True)
+# plt.savefig("GW1500914_Pv2.jpeg")
+
+###########################################
+############# Save the Run ################
+###########################################
+# import pickle
+# pickle.dump(result, open("GW150914_pv2.pkl", "wb"), protocol=pickle.HIGHEST_PROTOCOL)