def load_sample(aexps=aexps): cld = {} for aexp in aexps: GCD = GetClusterData(aexp=aexp, db_name=db_name, db_dir=db_dir, profiles_list=profiles_list, halo_properties_list=halo_properties_list) cld[GCD['aexp']] = GCD return cld
def load_sample(parsed_info): cld = {} for aexp in parsed_info['LoadedTrainingData']['aexps']: GCD = GetClusterData( aexp=aexp, db_name=parsed_info['DataBaseInfo']['db_name'], db_dir=parsed_info['DataBaseInfo']['db_dir'], profiles_list=parsed_info['LoadedTrainingData']['profiles_list'], halo_properties_list=parsed_info['LoadedTrainingData'] ['halo_properties_list']) cld[GCD['aexp']] = GCD return cld
axes=[[0.15, 0.4, 0.80, 0.55], [0.15, 0.15, 0.80, 0.24]], axes_labels=[Mgratio, fMgz1], ylog=[False, False], xlabel=r"$R/R_{500c}$", xlim=(0.2, 5), ylims=[(0.31, 1), (0.6, 1.4)]) Mg = {} Mgplots = [Mg] clkeys = ['Mg_bulk/Mg_all_500c'] linestyles = ['-'] for aexp in aexps: cldata = GetClusterData(aexp=aexp, db_name=db_name, db_dir=db_dir, profiles_list=profiles_list, halo_properties_list=halo_properties_list) Mg[aexp] = calculate_profiles_mean_variance(cldata['Mg_bulk/Mg_all_500c']) pa.axes[Mgratio].plot(rbins, Mg[aexp]['mean'], color=color(aexp), ls='-', label="$z=%3.1f$" % aexp2redshift(aexp)) for aexp in aexps: for Mg, ls in zip(Mgplots, linestyles): fractional_evolution = get_profiles_division_mean_variance( mean_profile1=Mg[aexp]['mean'],