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()