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])