"n_steps": 11,
}
pkl_file = make_analysis_pickle(mh_dict)
# Creates a Minimisation Handler using the dictionary, and runs the trials
# mh_power_law = MinimisationHandler.create(mh_dict_power_law)
# mh_power_law.iterate_run(mh_dict_power_law["scale"], n_steps=mh_dict_power_law["n_steps"],
# n_trials=mh_dict_power_law["n_trials"])
rd.submit_to_cluster(pkl_file, n_jobs=5)
res_dict[i] = mh_dict
rd.wait_for_cluster()
sens = []
disc = []
for i in n_range:
mh_dict = res_dict[i]
# Creates a Results Handler to analyse the results, and calculate the
# sensitivity. This is the flux that needs to arrive at Earth, in order for
# IceCube to see an overfluctuation 90% of the time. Prints this information.
rh = ResultsHandler(mh_dict)
sens.append(rh.sensitivity)
disc.append(rh.disc_potential)
# n_cpu=1 if cluster else 32,
# n_jobs=cluster,
# h_cpu='02:59:59'
# )
job_ids.append(job_id)
time_res[gamma] = mh_dict
cat_res[time_key] = time_res
full_res[cat] = cat_res
# Wait for cluster. If there are no cluster jobs, this just runs
if cluster and np.any(job_ids):
logging.info(f"waiting for jobs {job_ids}")
wait_for_cluster(job_ids)
stacked_sens = {}
stacked_sens_flux = {}
for b, (cat_name, cat_res) in enumerate(full_res.items()):
stacked_sens_flux[cat_name] = {}
for (time_key, time_res) in cat_res.items():
stacked_sens_flux[cat_name][time_key] = {}
sens_livetime = []
fracs = []
disc_pots_livetime = []
