root.root_open(get_root_file_name(args.root_out))
# run multinest
multinest.run(
LogLikelihood=myloglike,
Prior=myprior,
n_dims=len(param_ranges),
n_params=None,
n_clustering_params=None,
wrapped_params=None,
multimodal=args.multimodal,
const_efficiency_mode=args.const_efficiency_mode,
n_live_points=args.n_live_points,
evidence_tolerance=args.evidence_tolerance,
sampling_efficiency=args.sampling_efficiency,
n_iter_before_update=args.n_iter_before_update,
null_log_evidence=args.null_log_evidence, # -1e90,
max_modes=args.max_modes,
outputfiles_basename="{}/root-".format(args.multinest_dir), # 'root-' to get root-.txt
seed=args.seed,
verbose=("multinest" in args.verbose),
resume=args.resume,
context=args.context, # 0,
write_output=args.write_output, # True,
log_zero=args.log_zero, # -1e100,
max_iter=args.max_iter,
init_MPI=args.init_MPI, # False,
dump_callback=None,
)
# close root file after sampling
if args.root_out:
root.root_close()
nlive=nlive, tol=tolerance, eff=samplingefficiency, res=args.resume,
dataset=args.data_set,
seed=my_seed,
boundaries=bpp.pformat(get_param_ranges()),
)
fname='{}/mc_mn_info.txt'.format(args.multinest_dir)
with open(fname,'w') as info_file:
info_file.write(info)
#open root file before calling the sampling algorithm
if args.root_out:
root.root_open(args.root_out)
# run multinest
multinest.run(myloglike,
myprior,
n_dims = len(param_ranges),
resume = args.resume,
verbose = ('multinest' in args.verbose),
sampling_efficiency = samplingefficiency,
n_live_points = nlive ,
max_iter = args.max_iter,
seed = my_seed,
outputfiles_basename = '{}/'.format(args.multinest_dir),
evidence_tolerance = tolerance)
#close root file after sampling
if args.root_out:
root.root_close()
