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'],