ylims=[(0.1, 10.1), (0.6, 1.4)])
Smw = {}
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)
nu_cut_hids = nu_cut(nu=cldata['nu_500c'],
threshold=nu_threshold[nu_threshold_key])
pruned_profiles = prune_dict(d=cldata['S_mw/S500c'], k=nu_cut_hids)
Smw[aexp] = calculate_profiles_mean_variance(pruned_profiles)
pa.axes[Sratio].plot(rbins,
Smw[aexp]['mean'],
color=color(aexp),
ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
for aexp in aexps:
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=Smw[aexp]['mean'],
var_profile1=Smw[aexp]['var'],
mean_profile2=Smw[0.5]['mean'],
var_profile2=Smw[0.5]['var'],
)
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)
nu_cut_hids = nu_cut(nu=cldata['nu_500c'], threshold=nu_threshold[nu_threshold_key])
Mg[aexp] = calculate_profiles_mean_variance(prune_dict(d=cldata['Mg_bulk/Mg_all_500c'],
k=nu_cut_hids))
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'],
var_profile1=Mg[aexp]['var'],
mean_profile2=Mg[0.5]['mean'],
var_profile2=Mg[0.5]['var'],
)
ylims=[(1e-1,1e4),(0.6,1.4)])
rho={}
rhoplots = [rho]
clkeys = ['rhog_bulk/rho200m']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_200m'], threshold=nu_threshold[nu_threshold_key])
rho[aexp] = calculate_profiles_mean_variance(prune_dict(d=cldata['rhog_bulk/rho200m'],
k=nu_cut_hids))
pa.axes[rhoratio].plot( rbins, rho[aexp]['mean'],color=color(aexp),ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
for aexp in aexps :
for rho,ls in zip(rhoplots,linestyles) :
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=rho[aexp]['mean'],
var_profile1=rho[aexp]['var'],
mean_profile2=rho[0.5]['mean'],
var_profile2=rho[0.5]['var'],
)
ylims=[(0.,0.8),(0.6,1.4)])
TratioV2={}
plots=[TratioV2]
clkeys=['Tmw_Vcirc2_ratio_500c']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_500c'], threshold=nu_threshold)
for p, key in zip(plots,clkeys) :
pruned_profiles = prune_dict(d=cldata[key],k=nu_cut_hids)
p[aexp] = calculate_profiles_mean_variance(pruned_profiles)
pa.axes[Tmw_Vcirc2_ratio].plot( rbins, TratioV2[aexp]['mean'],
color=color(aexp),ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
pa.axes[Tmw_Vcirc2_ratio].fill_between(rbins, TratioV2[0.5]['down'],
TratioV2[0.5]['up'],
color=color(0.5), zorder=0)
for aexp in aexps :
fractional_evolution = get_profiles_division_mean_variance(
TratioV2 = {}
plots = [TratioV2]
clkeys = ['Ttot_Vcirc2_ratio_200m']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_200m'], threshold=nu_threshold)
for p, key in zip(plots, clkeys):
pruned_profiles = prune_dict(d=cldata[key], k=nu_cut_hids)
p[aexp] = calculate_profiles_mean_variance(pruned_profiles)
pa.axes[Ttot_Vcirc2_ratio].plot(rbins,
TratioV2[aexp]['mean'],
color=color(aexp),
ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
pa.axes[Ttot_Vcirc2_ratio].fill_between(rbins,
TratioV2[0.5]['down'],
TratioV2[0.5]['up'],
color=color(0.5),
zorder=0)
for aexp in aexps:
TratioV2 = {}
plots = [TratioV2]
clkeys = ['Tmw_Vr2_ratio_200m']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_200m'],
threshold=nu_threshold[nu_threshold_key])
Tmw = calculate_profiles_mean_variance(
prune_dict(d=cldata['Tmw_cm_per_s_2_r200m'], k=nu_cut_hids))
Vr2 = calculate_profiles_mean_variance(
prune_dict(cldata['Vr2_cm_per_s_2_r200m'], k=nu_cut_hids))
TratioV2[aexp] = get_profiles_division_mean_variance(
mean_profile1=Tmw['mean'],
var_profile1=Tmw['var'],
mean_profile2=Vr2['mean'],
var_profile2=Vr2['var'])
pa.axes[Tmw_Vr2_ratio].plot(rbins,
TratioV2[aexp]['mean'],
color=color(aexp),
ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
for aexp in aexps:
xlim=(0.2,2),
ylims=[(1e-1,1e2),(0.4,1.6)])
TratioV2={}
plots=[TratioV2]
clkeys=['Tmw_Vr2_ratio_500c']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_500c'], threshold=nu_threshold[nu_threshold_key])
Tmw = calculate_profiles_mean_variance(prune_dict(d=cldata['Tmw_cm_per_s_2_r500c'],
k=nu_cut_hids))
Vr2 = calculate_profiles_mean_variance(prune_dict(cldata['Vr2_cm_per_s_2_r500c'],
k=nu_cut_hids))
TratioV2[aexp] = get_profiles_division_mean_variance(
mean_profile1=Tmw['mean'], var_profile1=Tmw['var'],
mean_profile2=Vr2['mean'], var_profile2=Vr2['var'])
pa.axes[Tmw_Vr2_ratio].plot( rbins, TratioV2[aexp]['mean'],
color=color(aexp),ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
for aexp in aexps :
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=TratioV2[aexp]['mean'],
var_profile1=TratioV2[aexp]['var'],
rhoplots = [rho]
clkeys = ['rhog_bulk/rho500c']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_500c'],
threshold=nu_threshold[nu_threshold_key])
rho[aexp] = calculate_profiles_mean_variance(
prune_dict(d=cldata['rhog_bulk/rho500c'], k=nu_cut_hids))
pa.axes[rhoratio].plot(rbins,
rho[aexp]['mean'],
color=color(aexp),
ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
for aexp in aexps:
for rho, ls in zip(rhoplots, linestyles):
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=rho[aexp]['mean'],
var_profile1=rho[aexp]['var'],
mean_profile2=rho[0.5]['mean'],
var_profile2=rho[0.5]['var'],
)
xlim=(0.2,2),
ylims=[(1e-1,1e2),(0.4,1.6)])
TratioV2={}
plots=[TratioV2]
clkeys=['Tnt_Vr2_ratio_200m']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_200m'], threshold=nu_threshold[nu_threshold_key])
Tnt = calculate_profiles_mean_variance(prune_dict(d=cldata['Tnt_cm_per_s_2_r200m'],
k=nu_cut_hids))
Vr2 = calculate_profiles_mean_variance(prune_dict(d=cldata['Vr2_cm_per_s_2_r200m'],
k=nu_cut_hids))
TratioV2[aexp] = get_profiles_division_mean_variance(
mean_profile1=Tnt['mean'], var_profile1=Tnt['var'],
mean_profile2=Vr2['mean'], var_profile2=Vr2['var'])
print TratioV2[aexp]['mean']
pa.axes[Tnt_Vr2_ratio].plot( rbins, TratioV2[aexp]['mean'],
color=color(aexp),ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
for aexp in aexps :
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=TratioV2[aexp]['mean'],
var_profile1=TratioV2[aexp]['var'],
rhoplots = [rho]
clkeys = ['rhog_bulk/rho200m']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_200m'],
threshold=nu_threshold[nu_threshold_key])
rho[aexp] = calculate_profiles_mean_variance(
prune_dict(d=cldata['rhog_bulk/rho200m'], k=nu_cut_hids))
pa.axes[rhoratio].plot(rbins,
rho[aexp]['mean'],
color=color(aexp),
ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
for aexp in aexps:
for rho, ls in zip(rhoplots, linestyles):
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=rho[aexp]['mean'],
var_profile1=rho[aexp]['var'],
mean_profile2=rho[0.5]['mean'],
var_profile2=rho[0.5]['var'],
)
Mgplots = [Mg]
clkeys = ['Mg_bulk/Mg_all_200m']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_200m'],
threshold=nu_threshold[nu_threshold_key])
Mg[aexp] = calculate_profiles_mean_variance(
prune_dict(d=cldata['Mg_bulk/Mg_all_200m'], k=nu_cut_hids))
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'],
var_profile1=Mg[aexp]['var'],
mean_profile2=Mg[0.5]['mean'],
var_profile2=Mg[0.5]['var'],
)
ylims=[(1e-1,1e4),(0.6,1.4)])
rho={}
rhoplots = [rho]
clkeys = ['rhog_all/rho500c']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_500c'], threshold=nu_threshold[nu_threshold_key])
rho[aexp] = calculate_profiles_mean_variance(prune_dict(d=cldata['rhog_all/rho500c'],
k=nu_cut_hids))
pa.axes[rhoratio].plot( rbins, rho[aexp]['mean'],color=color(aexp),ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
for aexp in aexps :
for rho,ls in zip(rhoplots,linestyles) :
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=rho[aexp]['mean'],
var_profile1=rho[aexp]['var'],
mean_profile2=rho[0.5]['mean'],
var_profile2=rho[0.5]['var'],
)
ylims=[(1e-1,1e4),(0.6,1.4)])
rho={}
rhoplots = [rho]
clkeys = ['rhog_all/rho200m']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_200m'], threshold=nu_threshold[nu_threshold_key])
rho[aexp] = calculate_profiles_mean_variance(prune_dict(d=cldata['rhog_all/rho200m'],
k=nu_cut_hids))
pa.axes[rhoratio].plot( rbins, rho[aexp]['mean'],color=color(aexp),ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
for aexp in aexps :
for rho,ls in zip(rhoplots,linestyles) :
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=rho[aexp]['mean'],
var_profile1=rho[aexp]['var'],
mean_profile2=rho[0.5]['mean'],
var_profile2=rho[0.5]['var'],
)
TratioV2 = {}
plots = [TratioV2]
clkeys = ['Tnt_Vr2_ratio_500c']
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)
nu_cut_hids = nu_cut(nu=cldata['nu_500c'],
threshold=nu_threshold[nu_threshold_key])
Tnt = calculate_profiles_mean_variance(
prune_dict(d=cldata['Tnt_cm_per_s_2_r500c'], k=nu_cut_hids))
Vr2 = calculate_profiles_mean_variance(
prune_dict(d=cldata['Vr2_cm_per_s_2_r500c'], k=nu_cut_hids))
TratioV2[aexp] = get_profiles_division_mean_variance(
mean_profile1=Tnt['mean'],
var_profile1=Tnt['var'],
mean_profile2=Vr2['mean'],
var_profile2=Vr2['var'])
print TratioV2[aexp]['mean']
pa.axes[Tnt_Vr2_ratio].plot(rbins,
TratioV2[aexp]['mean'],
color=color(aexp),
ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
ylog=[True,False],
xlim=(0.2,5),
ylims=[(0.1,10.1),(0.6,1.4)])
Smw={}
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)
nu_cut_hids = nu_cut(nu=cldata['nu_500c'], threshold=nu_threshold[nu_threshold_key])
pruned_profiles = prune_dict(d=cldata['S_mw/S500c'],k=nu_cut_hids)
Smw[aexp] = calculate_profiles_mean_variance(pruned_profiles)
pa.axes[Sratio].plot( rbins, Smw[aexp]['mean'],color=color(aexp),ls='-',
label="$z=%3.1f$" % aexp2redshift(aexp))
for aexp in aexps :
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=Smw[aexp]['mean'],
var_profile1=Smw[aexp]['var'],
mean_profile2=Smw[0.5]['mean'],
var_profile2=Smw[0.5]['var'],
)
