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'],
)
pa.axes[fMgz1].plot( rbins, fractional_evolution['mean'],
color=color(aexp),ls=ls)
# Collect average profiles at each z
for Tplot, clkey in zip(Tplots,clkeys) :
Tplot[aexp] = calculate_profiles_mean_variance(cldata[clkey])
for aexp in aexps :
for T, axes_label in zip(Tplots,axes_labels) :
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=T[aexp]['mean'],
var_profile1=T[aexp]['var'],
mean_profile2=T[1.0]['mean'],
var_profile2=T[1.0]['var'],
)
pa.axes[axes_label].plot( rbins, fractional_evolution['mean'],
color=color(aexp),ls=':',label="$z=%3.1f$" % aexp2redshift(aexp))
for axes_label,delta,xloc in zip(axes_labels,deltas,text_xlocs) :
print axes_label, delta
pa.axes[axes_label].tick_params(labelsize=12)
pa.axes[axes_label].set_yticks(arange(0.8,1.5,0.2))
pa.axes[axes_label].text(xloc,1.3,'$\\Delta='+delta+'$')
pa.axes[axes_labels[1]].set_ylabel("$\\tilde{T}_{nt}/\\tilde{T}_{nt}(z=0)$",
fontsize="xx-large")
pa.set_legend(axes_label=axes_labels[0],ncol=3,loc='lower right', frameon=False)
pa.color_legend_texts(axes_label=axes_labels[0])
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'],
var_profile1=Mg[aexp]['var'],
mean_profile2=Mg[0.5]['mean'],
var_profile2=Mg[0.5]['var'],
)
pa.axes[fMgz1].plot(rbins,
fractional_evolution['mean'],
color=color(aexp),
ls=ls)
# Collect average profiles at each z
for Tplot, clkey in zip(Tplots,clkeys) :
Tplot[aexp] = calculate_profiles_mean_variance(cldata[clkey])
for aexp in aexps :
for T, axes_label in zip(Tplots,axes_labels) :
fractional_evolution = get_profiles_division_mean_variance(
mean_profile1=T[aexp]['mean'],
var_profile1=T[aexp]['var'],
mean_profile2=T[1.0]['mean'],
var_profile2=T[1.0]['var'],
)
pa.axes[axes_label].plot( rbins, fractional_evolution['mean'],
color=color(aexp),ls='-',label="$z=%3.1f$" % aexp2redshift(aexp))
for axes_label,delta,xloc in zip(axes_labels,deltas,text_xlocs) :
print axes_label, delta
pa.axes[axes_label].tick_params(labelsize=12)
pa.axes[axes_label].set_yticks(arange(0.8,1.4,0.2))
pa.axes[axes_label].text(xloc,1.2,'$\\Delta='+delta+'$')
pa.axes[axes_labels[1]].set_ylabel("$\\tilde{T}_{mw}/\\tilde{T}_{mw}(z=0)$",
fontsize="xx-large")
pa.set_legend(axes_label=axes_labels[0],ncol=3,loc='upper right', frameon=False)
pa.color_legend_texts(axes_label=axes_labels[0])
