def main():
parser = argparse.ArgumentParser(
description="Starts an LLH client, which then requests 3600 evals.")
parser.add_argument("-c",
"--conf_file",
type=str,
help="service configuration file",
required=True)
parser.add_argument("-d", "--data_file", type=str, help="test data file")
args = parser.parse_args()
with open(args.conf_file) as f:
params = json.load(f)
client = LLHClient(ctrl_addr=params["ctrl_addr"], conf_timeout=20000)
with open(args.data_file, "rb") as f:
event = pickle.load(f)[8]
hit_data = event["hits"]
evt_data = event["total_charge"]
theta = event["params"]
llhs = []
start = time.time()
for i in range(N_ITERATIONS):
llhs.append(client.eval_llh(hit_data, evt_data, theta))
delta = time.time() - start
print(f"{N_ITERATIONS} evals took {delta*1000:.3f} ms"
f" ({delta/N_ITERATIONS*1e3:.3f} ms per eval)")
class I3FreeDOMClient:
"""FreeDOM client IceTray module. Connects to a running LLHService"""
def __init__(self, ctrl_addr, conf_timeout, rng=None):
"""initialize FreeDOM client, connect to LLH service"""
self._llh_client = LLHClient(ctrl_addr, conf_timeout)
if rng is None:
self._rng = np.random.default_rng(None)
else:
self._rng = rng
def __call__(
self,
frame,
geo,
reco_pulse_series_name,
suffix="",
init_range=DEFAULT_INIT_RANGE,
search_limits=DEFAULT_SEARCH_LIMITS,
n_live_points=DEFAULT_N_LIVE_POINTS,
do_postfit=True,
store_all=False,
truth_seed=False,
batch_size=DEFAULT_BATCH_SIZE,
par_transforms=None,
do_track_dllh=False,
**crs_fit_kwargs,
):
"""reconstruct an event stored in an i3frame"""
event = i3frame_dataloader.load_event(frame, geo,
reco_pulse_series_name)
fit_kwargs = dict(
event=event,
clients=[self._llh_client],
rng=self._rng,
init_range=init_range,
search_limits=search_limits,
n_live_points=n_live_points,
do_postfit=do_postfit,
store_all=store_all,
truth_seed=truth_seed,
param_transforms=par_transforms,
batch_size=batch_size,
spherical_indices=DEFAULT_SPHERICAL_INDICES,
max_iter=DEFAULT_MAX_ITER,
**crs_fit_kwargs,
)
full_res = timed_fit(**fit_kwargs)
if event["params"] is not None:
full_res["truth_LLH"] = self._llh_client.eval_llh(
event["hit_data"][0], event["evt_data"][0], event["params"])
prefix = f"FreeDOM_{suffix}_"
store_fit_result(frame,
prefix,
full_res,
par_transforms,
store_i3_particles=True)
if do_track_dllh:
# do not conduct postfit for zero_track fits
fit_kwargs["do_postfit"] = False
no_track_res, E_only_res = zero_track_fit(full_res, **fit_kwargs)
store_dllh(frame, prefix, full_res, no_track_res, E_only_res)
store_fit_result(frame, prefix + "no_track_", no_track_res,
par_transforms)
store_fit_result(frame, prefix + "E_only_", E_only_res,
par_transforms)