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generate_photons.py
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generate_photons.py
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"""Generate photons for a range of emitter-reciever distances."""
from jax.config import config as jax_conf
jax_conf.update("jax_enable_x64", True)
import pickle
import json
import jax
import jax.numpy as jnp
import numpy as np
import os
from argparse import ArgumentParser
from scipy.stats import qmc
from tqdm import tqdm
from jax import jit, vmap
from hyperion.propagate import (
collect_hits,
initialize_direction_isotropic,
make_cherenkov_spectral_sampling_func,
make_fixed_pos_time_initializer,
make_loop_for_n_steps,
make_photon_sphere_intersection_func,
make_photon_trajectory_fun,
make_step_function,
)
from hyperion.medium import medium_collections
from hyperion.utils import calculate_min_number_steps
parser = ArgumentParser()
parser.add_argument("-o", "--outfile", required=True, dest="outfile")
parser.add_argument("-s", "--seed", type=int, required=False, dest="seed", default=0)
parser.add_argument(
"-d", "--dist", type=float, required=False, dest="dist", default=None
)
parser.add_argument(
"--photons_per_batch", type=float, required=False, dest="ph_per_batch", default=1e7
)
parser.add_argument(
"--n_photon_batches", type=int, required=False, dest="n_ph_batches", default=5000
)
parser.add_argument(
"--max_dist", type=float, required=False, dest="max_dist", default=500
)
parser.add_argument(
"--min_dist", type=float, required=False, dest="min_dist", default=1
)
parser.add_argument("-c", "--config", type=str, required=True, dest="config")
args = parser.parse_args()
if jax.default_backend() == "cpu":
raise RuntimeError("Running on CPU. Bailing...")
path_to_config = os.path.join(os.path.dirname(__file__), f"data/{args.config}")
config = json.load(open(path_to_config))["photon_propagation"]
outfile = open(args.outfile, "wb")
outfile.close()
emitter_x = jnp.array([0, 0, 0.0])
emitter_t = 0.0
ref_ix_f, sca_a_f, sca_l_f, _ = medium_collections[config["medium"]]
wavelength_init = make_cherenkov_spectral_sampling_func(
config["wavelength_range"], ref_ix_f
)
photon_init = make_fixed_pos_time_initializer(
emitter_x, emitter_t, initialize_direction_isotropic, wavelength_init
)
if args.dist is None:
# Use quasi-random numbers to select random distances with optimal coverage
# in log space.
sampler = qmc.Sobol(d=1, scramble=False)
sample = sampler.random_base2(m=1)
dists = (
10 ** qmc.scale(sample, np.log10(args.min_dist), np.log10(args.max_dist))
).squeeze()
else:
dists = [args.dist]
training_data = []
all_data = []
for det_dist in tqdm(dists, total=len(dists), disable=True):
det_pos = jnp.array([0, 0, det_dist])
intersection_f = make_photon_sphere_intersection_func(
det_pos, config["module_radius"]
)
step_fun = make_step_function(
intersection_f=intersection_f,
scattering_function=sca_a_f,
scattering_length_function=sca_l_f,
ref_index_func=ref_ix_f,
)
n_steps = calculate_min_number_steps(
ref_ix_f,
sca_l_f,
det_dist,
500,
config["wavelength_range"][0],
0.01,
)
print(f"Propagation steps: {n_steps}")
loop_func = make_loop_for_n_steps(n_steps)
trajec_fun = make_photon_trajectory_fun(
step_fun,
photon_init,
loop_func=loop_func,
)
trajec_fun_v = jit(vmap(trajec_fun, in_axes=[0]))
(
times,
arrival_angles,
emission_angles,
steps,
positions,
sims_cnt,
wavelengths,
) = collect_hits(trajec_fun_v, args.ph_per_batch, args.n_ph_batches, args.seed)
all_data.append(
{
"dist": det_dist,
"times_det": times,
"arrival_angles": arrival_angles,
"emission_angles": emission_angles,
"photon_steps": steps,
"positions_det": positions,
"nphotons_sim": sims_cnt * args.ph_per_batch,
"wavelengths": wavelengths,
}
)
with open(args.outfile, "wb") as outfile:
pickle.dump(all_data, outfile)