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'], )