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Thibeau Wouters
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May 8, 2024
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import psutil | ||
p = psutil.Process() | ||
p.cpu_affinity([0]) | ||
import os | ||
os.environ["CUDA_VISIBLE_DEVICES"] = "2" | ||
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.10" | ||
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import time | ||
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import jax | ||
import jax.numpy as jnp | ||
import optax | ||
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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 RippleTaylorF2 | ||
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 | ||
duration = 4 | ||
post_trigger_duration = 2 | ||
start_pad = duration - post_trigger_duration | ||
end_pad = post_trigger_duration | ||
fmin = 20.0 | ||
fmax = 1024.0 | ||
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ifos = ["H1", "L1"] | ||
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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) | ||
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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"]) | ||
lambda1_prior = Unconstrained_Uniform(0.0, 5000.0, naming=["lambda_1"]) | ||
lambda2_prior = Unconstrained_Uniform(0.0, 5000.0, naming=["lambda_2"]) | ||
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 | ||
), | ||
) | ||
}, | ||
) | ||
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prior = Composite( | ||
[ | ||
Mc_prior, | ||
q_prior, | ||
s1z_prior, | ||
s2z_prior, | ||
lambda1_prior, | ||
lambda2_prior, | ||
dL_prior, | ||
t_c_prior, | ||
phase_c_prior, | ||
cos_iota_prior, | ||
psi_prior, | ||
ra_prior, | ||
sin_dec_prior, | ||
] | ||
) | ||
likelihood = TransientLikelihoodFD( | ||
[H1, L1], | ||
waveform=RippleTaylorF2(), | ||
trigger_time=gps, | ||
duration=4, | ||
post_trigger_duration=2, | ||
) | ||
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n_dim = 13 | ||
mass_matrix = jnp.eye(n_dim) | ||
mass_matrix = mass_matrix.at[0,0].set(1e-5) | ||
mass_matrix = mass_matrix.at[1,1].set(1e-4) | ||
mass_matrix = mass_matrix.at[2,2].set(1e-3) | ||
mass_matrix = mass_matrix.at[3,3].set(1e-3) | ||
mass_matrix = mass_matrix.at[7,7].set(1e-5) | ||
mass_matrix = mass_matrix.at[11,11].set(1e-2) | ||
mass_matrix = mass_matrix.at[12,12].set(1e-2) | ||
local_sampler_arg = {"step_size": mass_matrix * 1e-3} | ||
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# Build the learning rate scheduler | ||
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n_loop_training = 100 | ||
n_epochs = 100 | ||
total_epochs = n_epochs * n_loop_training | ||
start = int(total_epochs / 10) | ||
start_lr = 1e-3 | ||
end_lr = 1e-5 | ||
power = 4.0 | ||
schedule_fn = optax.polynomial_schedule( | ||
start_lr, end_lr, power, total_epochs-start, transition_begin=start) | ||
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jim = Jim( | ||
likelihood, | ||
prior, | ||
n_loop_training=n_loop_training, | ||
n_loop_production=20, | ||
n_local_steps=10, | ||
n_global_steps=1000, | ||
n_chains=1000, | ||
n_epochs=n_epochs, | ||
learning_rate=schedule_fn, | ||
n_max_examples=30000, | ||
n_flow_samples=100000, | ||
momentum=0.9, | ||
batch_size=30000, | ||
use_global=True, | ||
train_thinning=20, | ||
output_thinning=50, | ||
local_sampler_arg=local_sampler_arg, | ||
) | ||
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jim.sample(jax.random.PRNGKey(24)) | ||
jim.print_summary() |
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import psutil | ||
p = psutil.Process() | ||
p.cpu_affinity([0]) | ||
import os | ||
os.environ["CUDA_VISIBLE_DEVICES"] = "3" | ||
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.10" | ||
from jimgw.jim import Jim | ||
from jimgw.single_event.detector import H1, L1, V1 | ||
from jimgw.single_event.likelihood import HeterodynedTransientLikelihoodFD | ||
from jimgw.single_event.waveform import RippleTaylorF2 | ||
from jimgw.prior import Uniform, Composite | ||
import jax.numpy as jnp | ||
import jax | ||
import time | ||
jax.config.update("jax_enable_x64", True) | ||
import numpy as np | ||
import optax | ||
from gwosc.datasets import event_gps | ||
print(f"GPU found? {jax.devices()}") | ||
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data_path = "/home/thibeau.wouters/gw-datasets/GW170817/" # on CIT | ||
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start_runtime = time.time() | ||
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############ | ||
### BODY ### | ||
############ | ||
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### Data definitions | ||
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total_time_start = time.time() | ||
gps = 1187008882.43 | ||
trigger_time = gps | ||
fmin = 20 | ||
fmax = 2048 | ||
minimum_frequency = fmin | ||
maximum_frequency = fmax | ||
duration = 128 | ||
# epoch = duration - post_trigger_duration | ||
post_trigger_duration = 32 | ||
start_pad = duration - post_trigger_duration | ||
end_pad = post_trigger_duration | ||
f_ref = fmin | ||
tukey_alpha = 2 / (duration / 2) | ||
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ifos = ["H1", "L1"]#, "V1"] | ||
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H1.load_data(gps, start_pad, end_pad, fmin, fmax, psd_pad=4*duration, tukey_alpha=tukey_alpha, gwpy_kwargs={"version": 2, "cache": False}) | ||
L1.load_data(gps, start_pad, end_pad, fmin, fmax, psd_pad=4*duration, tukey_alpha=tukey_alpha, gwpy_kwargs={"version": 2, "cache": False}) | ||
# V1.load_data(gps, start_pad, end_pad, fmin, fmax, psd_pad=16, tukey_alpha=0.05) | ||
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### Define priors | ||
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# Internal parameters | ||
Mc_prior = Uniform(1.18, 1.21, naming=["M_c"]) | ||
q_prior = Uniform( | ||
0.125, | ||
1.0, | ||
naming=["q"], | ||
transforms={"q": ("eta", lambda params: params["q"] / (1 + params["q"]) ** 2)}, | ||
) | ||
s1z_prior = Uniform(-0.05, 0.05, naming=["s1_z"]) | ||
s2z_prior = Uniform(-0.05, 0.05, naming=["s2_z"]) | ||
lambda_1_prior = Uniform(0.0, 5000.0, naming=["lambda_1"]) | ||
lambda_2_prior = Uniform(0.0, 5000.0, naming=["lambda_2"]) | ||
dL_prior = Uniform(1.0, 75.0, naming=["d_L"]) | ||
# dL_prior = PowerLaw(1.0, 75.0, 2.0, naming=["d_L"]) | ||
t_c_prior = Uniform(-0.1, 0.1, naming=["t_c"]) | ||
phase_c_prior = Uniform(0.0, 2 * jnp.pi, naming=["phase_c"]) | ||
cos_iota_prior = 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 = Uniform(0.0, jnp.pi, naming=["psi"]) | ||
ra_prior = Uniform(0.0, 2 * jnp.pi, naming=["ra"]) | ||
sin_dec_prior = 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 | ||
), | ||
) | ||
}, | ||
) | ||
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prior_list = [ | ||
Mc_prior, | ||
q_prior, | ||
s1z_prior, | ||
s2z_prior, | ||
lambda_1_prior, | ||
lambda_2_prior, | ||
dL_prior, | ||
t_c_prior, | ||
phase_c_prior, | ||
cos_iota_prior, | ||
psi_prior, | ||
ra_prior, | ||
sin_dec_prior, | ||
] | ||
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prior = Composite(prior_list) | ||
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# The following only works if every prior has xmin and xmax property, which is OK for Uniform and Powerlaw | ||
bounds = jnp.array([[p.xmin, p.xmax] for p in prior.priors]) | ||
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### Create likelihood object | ||
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ref_params = { | ||
'M_c': 1.19793583, | ||
'eta': 0.24794374, | ||
's1_z': 0.00220637, | ||
's2_z': 0.0499, | ||
'lambda_1': 605.12916663, | ||
'lambda_2': 405.12916663, | ||
'd_L': 45.41592353, | ||
't_c': 0.00220588, | ||
'phase_c': 5.76822606, | ||
'iota': 2.46158044, | ||
'psi': 2.09118099, | ||
'ra': 5.03335133, | ||
'dec': 0.01679998 | ||
} | ||
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n_bins = 100 | ||
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likelihood = HeterodynedTransientLikelihoodFD([H1, L1], prior=prior, bounds=bounds, waveform=RippleTaylorF2(f_ref=f_ref), trigger_time=gps, duration=duration, n_bins=n_bins, ref_params=ref_params) | ||
print("Running with n_bins = ", n_bins) | ||
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# Local sampler args | ||
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eps = 1e-3 | ||
n_dim = 13 | ||
mass_matrix = jnp.eye(n_dim) | ||
mass_matrix = mass_matrix.at[0,0].set(1e-5) | ||
mass_matrix = mass_matrix.at[1,1].set(1e-4) | ||
mass_matrix = mass_matrix.at[2,2].set(1e-3) | ||
mass_matrix = mass_matrix.at[3,3].set(1e-3) | ||
mass_matrix = mass_matrix.at[7,7].set(1e-5) | ||
mass_matrix = mass_matrix.at[11,11].set(1e-2) | ||
mass_matrix = mass_matrix.at[12,12].set(1e-2) | ||
local_sampler_arg = {"step_size": mass_matrix * eps} | ||
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# Build the learning rate scheduler | ||
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n_loop_training = 200 | ||
n_epochs = 50 | ||
total_epochs = n_epochs * n_loop_training | ||
start = int(total_epochs / 10) | ||
start_lr = 1e-3 | ||
end_lr = 1e-5 | ||
power = 4.0 | ||
schedule_fn = optax.polynomial_schedule( | ||
start_lr, end_lr, power, total_epochs-start, transition_begin=start) | ||
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scheduler_str = f"polynomial_schedule({start_lr}, {end_lr}, {power}, {total_epochs-start}, {start})" | ||
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# Create jim object | ||
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outdir_name = "./outdir/" | ||
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jim = Jim( | ||
likelihood, | ||
prior, | ||
n_loop_training=n_loop_training, | ||
n_loop_production=20, | ||
n_local_steps=10, | ||
n_global_steps=500, | ||
n_chains=1000, | ||
n_epochs=n_epochs, | ||
learning_rate=schedule_fn, | ||
max_samples=50000, | ||
momentum=0.9, | ||
batch_size=50000, | ||
use_global=True, | ||
keep_quantile=0.0, | ||
train_thinning=10, | ||
output_thinning=30, | ||
local_sampler_arg=local_sampler_arg, | ||
stopping_criterion_global_acc = 0.20, | ||
outdir_name=outdir_name | ||
) | ||
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### Sample and show results | ||
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jim.sample(jax.random.PRNGKey(41)) | ||
jim.print_summary() |
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