def __init__(self, n_cpu, **kwargs):
self.queue = JoinableQueue()
self.log_queue = Queue()
self.n_tasks = Value('i', 0)
kwargs["n_tasks"] = self.n_tasks
self.processes = [
Process(target=self.run_trial, kwargs=kwargs)
for _ in range(int(n_cpu))
]
self.mh = MinimisationHandler.create(kwargs["mh_dict"])
for season in self.mh.seasons.keys():
inj = self.mh.get_injector(season)
inj.calculate_n_exp()
self.mh_dict = kwargs["mh_dict"]
self.scales = []
handler = logging.StreamHandler()
handler.setFormatter(
logging.Formatter(
"%(levelname)s: %(asctime)s - %(process)s - %(message)s"))
# ql gets records from the queue and sends them to the handler
ql = QueueListener(self.log_queue, handler)
ql.start()
for p in self.processes:
p.start()
def do_asimov_scale_estimation(self):
"""estimate the injection scale using Asimov estimation"""
logger.info("doing asimov estimation")
mh = MinimisationHandler.create(self.mh_dict)
scale_estimate = mh.guess_scale()
logger.debug(f"estimated scale: {scale_estimate}")
self.disc_guess = scale_estimate
self.sens_guess = 0.3 * self.disc_guess
"name": name,
"mh_name": "fixed_weights",
"datasets": [IC86_1_dict],
"catalogue": ps_catalogue_name(sin_dec),
"llh_dict": llh_dict,
"inj kwargs": inj_dict,
"n_trials": 20,
"n_steps": 15,
"scale": scale,
}
pkl_file = make_analysis_pickle(mh_dict)
# rd.submit_to_cluster(pkl_file, n_jobs=150)
#
mh = MinimisationHandler.create(mh_dict)
# mh.iterate_run(n_steps=2, n_trials=20, scale=scale)
mh.run(10, scale=float(scale))
config_mh.append(mh_dict)
res_dict[key] = config_mh
all_res[gamma] = res_dict
rd.wait_for_cluster()
for (gamma, res_dict) in all_res.items():
gamma_name = basename + str(gamma) + "/"
os.makedirs(os.path.dirname(ts_path))
except OSError:
pass
if os.path.isfile(ts_path):
with open(ts_path, "r") as f:
print("Loading ts_array")
ts_array = Pickle.load(f)
else:
print("Empty TS array")
ts_array = []
# Creates a Minimisation Handler using the dictionary, and runs the trials
mh_pl = MinimisationHandler.create(mh_dict_pl)
mh_tm = MinimisationHandler.create(mh_dict_tm)
n_trials = 100
for i in range(n_trials):
seed = random.randint(0, 999999)
mh_pl.set_random_seed(seed)
res_pl = mh_pl.run_trial(scale=1.0)
mh_tm.set_random_seed(seed)
res_tm = mh_tm.run_trial(scale=1.0)
ts = res_tm["TS"] - res_pl["TS"]
print(i, seed, res_tm, res_pl, ts)
ts_array.append(ts)
