def test_ancestors_without_descendant(nsnap_ancestor = 20, scatter = 0.0, source='li-drory-march'):
'''
What happens to ancestors that do not have descendants at nsnap = 1
Notes
-----
* More than 50% of the subhalos at nsnap=20 do not have descendants at nsnap = 1.
What happens to them?
* Do they become satellites? --> Some become satellites, others
with no mass subhalos don't stay in the catalog at all
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
# Central subhalo at nsnap_ancestor (ancesotr)
anc = CentralSubhalos()
anc.read(nsnap_ancestor, scatter=scatter, source=source)
# Centrals subhalo at nsnap = 1 (descendant)
des = CentralSubhalos()
des.read(1, scatter=scatter, source=source)
no_descendants = np.invert(np.in1d(anc.index, des.ancestor20))
massive_nodescendants = np.where(anc.mass[no_descendants] > 0.0)
print 'N_SH no descendants ', len(anc.mass[no_descendants])
print 'N_SH total ', len(anc.mass)
print np.float(len(anc.mass[no_descendants]))/np.float(len(anc.mass))
no_des_index = anc.index[no_descendants][massive_nodescendants][:5]
print anc.mass[no_descendants][massive_nodescendants][:5]
for isnap in range(1, nsnap_ancestor)[::-1]:
i_des = CentralSubhalos()
i_des.read(isnap, scatter=scatter, source=source)
#print isnap, np.in1d(no_des_index, i_des.ancestor20)
#if not np.in1d(no_des_index, i_des.ancestor20)[0]:
des_sh = Subhalos()
des_sh.read(isnap, scatter=scatter, source=source)
#if np.sum(np.in1d(des_sh.ancestor20, no_des_index)) != len(no_des_index):
# raise ValueError
print des_sh.ilk[np.in1d(des_sh.ancestor20, no_des_index)][np.argsort(des_sh.ancestor20[np.in1d(des_sh.ancestor20, no_des_index)])]
print 'M* ', des_sh.mass[np.in1d(des_sh.ancestor20, no_des_index)][np.argsort(des_sh.ancestor20[np.in1d(des_sh.ancestor20, no_des_index)])]
print 'M_halo ', getattr(des_sh, 'halo.m')[np.in1d(des_sh.ancestor20, no_des_index)][np.argsort(des_sh.ancestor20[np.in1d(des_sh.ancestor20, no_des_index)])]
#print des_sh.mass[np.in1d(des_sh.index, no_des_index)]
#np.in1d(i_des.ancestor20, no_des_index)
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([6.0, 12.0])
sub.legend(loc='upper right')
def CentralSubhalo(self, nsnap, subhalo_prop=None):
''' Plot SMF of Central Subhalos
'''
if subhalo_prop is None:
raise ValueError
censub = CentralSubhalos()
censub.Read(nsnap,
scatter=subhalo_prop['scatter'],
source=subhalo_prop['source'],
nsnap_ancestor=subhalo_prop['nsnap_ancestor'])
self.plot(mass=censub.mass,
line_color='k', line_style='-', label='Central Subhalos')
return None
def LineageSMF(nsnap_ancestor,
descendants=None,
subhalo_prop={
'scatter': 0.0,
'source': 'li-march'
},
sfr_prop={
'fq': {
'name': 'wetzelsmooth'
},
'sfr': {
'name': 'average'
}
}):
''' Plot the SMF of lineage galaxy population (both ancestor and descendants).
Compare the lineage SMF to the central subhalo and analytic SMFs for all
subhalos. The main agreement, is between the lineage SMF and the central subhalo
SMF. If nsnap_ancestor is high, then there should be more discrepancy
between LIneage SMF and CentralSubhalos SMF because more galaxies are lost.
'''
# read in desendants from the lineage object
if descendants is None:
descendants = range(1, nsnap_ancestor)
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor,
subhalo_prop=subhalo_prop)
bloodline.Read(descendants)
smf_plot = plots.PlotSMF()
# plot ancestor against the analytic SMF
ancestor = getattr(bloodline, 'ancestor')
#smf_plot.cenque(ancestor) # SMF of lineage ancestor
#smf_plot.analytic(
# ancestor.zsnap,
# source=ancestor.subhalo_prop['source'],
# line_style='-', lw=1,
# label='All Subhalos')
#smf = SMF()
#subh = CentralSubhalos()
#subh.Read(
# nsnap_ancestor,
# scatter=ancestor.subhalo_prop['scatter'],
# source=ancestor.subhalo_prop['source'])
#subh_mass, subh_phi = smf.centralsubhalos(subh)
#smf_plot.sub.plot(subh_mass, subh_phi, lw=4, ls='--', c='gray', label='Central Subhalos')
for isnap in descendants:
descendant = getattr(bloodline, 'descendant_snapshot' + str(isnap))
smf = SMF()
subh = CentralSubhalos()
subh.Read(isnap,
scatter=descendant.subhalo_prop['scatter'],
source=descendant.subhalo_prop['source'])
subh_mass, subh_phi = smf.Obj(subh)
smf_plot.sub.plot(subh_mass, subh_phi, lw=4, ls='--', c='gray')
#print (subh_phi - d_phi)/d_phi
#smf_plot.analytic(
# descendant.zsnap,
# source=descendant.subhalo_prop['source'],
# line_style='-', lw=1,
# label=None)
smf_plot.set_axes()
plt.show()
smf_plot_file = ''.join([
'figure/test/', 'LineageSMF', '.ancestor',
str(nsnap_ancestor),
bloodline._file_spec(subhalo_prop=bloodline.subhalo_prop,
sfr_prop=bloodline.sfr_prop), '.png'
])
smf_plot.save_fig(smf_plot_file)
return None
def test_ancestors_without_descendant(nsnap_ancestor=20,
scatter=0.0,
source='li-drory-march'):
'''
What happens to ancestors that do not have descendants at nsnap = 1
Notes
-----
* More than 50% of the subhalos at nsnap=20 do not have descendants at nsnap = 1.
What happens to them?
* Do they become satellites? --> Some become satellites, others
with no mass subhalos don't stay in the catalog at all
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
# Central subhalo at nsnap_ancestor (ancesotr)
anc = CentralSubhalos()
anc.read(nsnap_ancestor, scatter=scatter, source=source)
# Centrals subhalo at nsnap = 1 (descendant)
des = CentralSubhalos()
des.read(1, scatter=scatter, source=source)
no_descendants = np.invert(np.in1d(anc.index, des.ancestor20))
massive_nodescendants = np.where(anc.mass[no_descendants] > 0.0)
print 'N_SH no descendants ', len(anc.mass[no_descendants])
print 'N_SH total ', len(anc.mass)
print np.float(len(anc.mass[no_descendants])) / np.float(len(anc.mass))
no_des_index = anc.index[no_descendants][massive_nodescendants][:5]
print anc.mass[no_descendants][massive_nodescendants][:5]
for isnap in range(1, nsnap_ancestor)[::-1]:
i_des = CentralSubhalos()
i_des.read(isnap, scatter=scatter, source=source)
#print isnap, np.in1d(no_des_index, i_des.ancestor20)
#if not np.in1d(no_des_index, i_des.ancestor20)[0]:
des_sh = Subhalos()
des_sh.read(isnap, scatter=scatter, source=source)
#if np.sum(np.in1d(des_sh.ancestor20, no_des_index)) != len(no_des_index):
# raise ValueError
print des_sh.ilk[np.in1d(des_sh.ancestor20, no_des_index)][np.argsort(
des_sh.ancestor20[np.in1d(des_sh.ancestor20, no_des_index)])]
print 'M* ', des_sh.mass[np.in1d(
des_sh.ancestor20, no_des_index)][np.argsort(
des_sh.ancestor20[np.in1d(des_sh.ancestor20, no_des_index)])]
print 'M_halo ', getattr(des_sh, 'halo.m')[np.in1d(
des_sh.ancestor20,
no_des_index)][np.argsort(des_sh.ancestor20[np.in1d(
des_sh.ancestor20, no_des_index)])]
#print des_sh.mass[np.in1d(des_sh.index, no_des_index)]
#np.in1d(i_des.ancestor20, no_des_index)
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([6.0, 12.0])
sub.legend(loc='upper right')
def OrphanSubhaloSMF(type,
nsnap_ancestor=20,
scatter=0.0,
source='li-drory-march'):
''' SMF for orphan central subhalos that do not have ancestors at snapshot
nsnap_ancestor.
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
# Central subhalo at nsnap_ancestor
if type == 'central':
subh = CentralSubhalos()
elif type == 'all':
subh = Subhalos()
subh.Read(nsnap_ancestor,
scatter=scatter,
source=source,
nsnap_ancestor=nsnap_ancestor)
ancestor_index = subh.index
ancestor_mass = subh.mass
for i_snap in [1, 5, 10, 15]:
if i_snap >= nsnap_ancestor:
continue
smf = SMF()
if type == 'central':
subh = CentralSubhalos()
elif type == 'all':
subh = Subhalos()
subh.Read(i_snap,
scatter=scatter,
source=source,
nsnap_ancestor=nsnap_ancestor)
# total central subhalo SMF
subh_mass, subh_phi = smf.centralsubhalos(subh)
sub.plot(subh_mass, subh_phi, lw=4, c=pretty_colors[i_snap], alpha=0.5)
# SMF of central subhalos who's ancestors are centrals at snapshot 20
if i_snap == 1:
label = 'Galaxies that do not have Ancestors'
else:
label = None
orphan = np.invert(
np.in1d(getattr(subh, 'ancestor' + str(nsnap_ancestor)),
ancestor_index))
orph_mass, orph_phi = smf.smf(subh.mass[orphan])
sub.plot(orph_mass,
orph_phi,
lw=2,
ls='--',
c=pretty_colors[i_snap],
label=label)
anc_mass, anc_phi = smf.smf(ancestor_mass)
sub.plot(anc_mass, anc_phi, lw=4, c='gray', label='Total')
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([6.0, 12.0])
sub.legend(loc='upper right')
fig_file = ''.join([
'figure/test/'
'OrphanSubhaloSMF', '.', type, '_subhalo', '.scatter',
str(round(scatter, 2)), '.ancestor',
str(nsnap_ancestor), '.', source, '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def SubhaloSMF(type, scatter=0.0, source='li-drory-march', nsnap_ancestor=20):
''' Test the Subhalos/CentralSubhalos imported from TreePM by
comparing their measured SMFs to the analytic SMFs
Parameters
----------
type : string
String that specifies whether we're interested in CentralSubhalos
or all Subhalos.
scatter : float
Float that specifies the scatter in the SMHM relation, which
affects the SHAM masses
source : string
String that specifies which SMF is used for SHAM
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
for i_snap in range(1, nsnap_ancestor + 1):
smf = SMF()
if type == 'all':
subh = Subhalos()
elif type == 'central':
subh = CentralSubhalos()
else:
raise ValueError
subh.Read(i_snap,
scatter=scatter,
source=source,
nsnap_ancestor=nsnap_ancestor)
subh_mass, subh_phi = smf.Obj(subh, dlogm=0.1, LF=False)
sub.plot(subh_mass,
subh_phi,
lw=4,
c=pretty_colors[i_snap % 19],
alpha=0.5)
analytic_mass, analytic_phi = smf.analytic(get_z_nsnap(i_snap),
source=source)
sub.plot(analytic_mass,
analytic_phi,
lw=4,
ls='--',
c=pretty_colors[i_snap % 19],
label=r"$ z = " + str(round(get_z_nsnap(i_snap), 2)) + "$")
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([6.0, 12.0])
#sub.legend(loc='upper right')
if type == 'all':
subhalo_str = 'subhalo'
elif type == 'central':
subhalo_str = 'central_subhalo'
else:
raise ValueError
plt.show()
fig_file = ''.join([
'figure/test/'
'SubhaloSMF', '.', subhalo_str, '.scatter',
str(round(scatter, 2)), '.ancestor',
str(nsnap_ancestor), '.', source, '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def DescendantSubhaloSMF(type,
nsnap_ancestor=20,
scatter=0.0,
source='li-drory-march',
nomass=False):
''' SMF for 'descendant' all/central subhalos that have ancestors. If nomass is
specified, then we highlight the portion of the SMF from galaxies whose
ancestors have M* = 0 at snapshot nsnap_ancestor.
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
# Central subhalo at nsnap_ancestor
if type == 'central':
subh = CentralSubhalos()
elif type == 'all':
subh = Subhalos()
# read in ancestor snapshot
subh.Read(nsnap_ancestor,
scatter=scatter,
source=source,
nsnap_ancestor=nsnap_ancestor)
ancestor_index = subh.index
ancestor_mass = subh.mass
# galaxy has M* = 0 at nsnap_ancestor
nomass = np.where(subh.mass == 0.0)
nomass_anc_index = subh.index[nomass]
nomass_anc_mass = subh.mass[nomass]
# galaxy has M* > 0 at nsnap_ancestor
massive = np.where(subh.mass > 0.0)
massive_anc_index = subh.index[massive]
massive_anc_mass = subh.mass[massive]
for i_snap in [1, 5, 10, 15]:
if i_snap == 1:
massive_label = 'Galaxies whose Ancestors have M* > 0'
nomass_label = 'Galaxies whose Ancestors have M* = 0'
label = 'Total'
elif i_snap >= nsnap_ancestor:
continue
else:
massive_label = None
nomass_label = None
label = None
smf = SMF()
# total central subhalo SMF
if type == 'central':
subh = CentralSubhalos()
elif type == 'all':
subh = Subhalos()
subh.Read(i_snap,
scatter=scatter,
source=source,
nsnap_ancestor=nsnap_ancestor)
subh_mass, subh_phi = smf.centralsubhalos(subh)
sub.plot(subh_mass,
subh_phi,
lw=3,
c=pretty_colors[i_snap],
label=label)
anc_ind = getattr(subh, 'ancestor' + str(nsnap_ancestor))
if not nomass:
# SMF of central subhalos who's ancestors are massive centrals at snapshot 20
if i_snap == 1:
has_ancestor, has_descendant = intersection_index(
anc_ind, massive_anc_index)
else:
has_ancestor, has_d = intersection_index(
anc_ind, massive_anc_index)
mass, phi = smf.smf(subh.mass[has_ancestor])
sub.plot(mass,
phi,
lw=2,
ls='--',
c=pretty_colors[i_snap],
label=massive_label)
if nomass:
# SMF of central subhalos who's ancestors have M*=0 centrals at snapshot 20
if i_snap == 1:
has_ancestor, has_descendant = intersection_index(
anc_ind, nomass_anc_index)
else:
has_ancestor, has_d = intersection_index(
anc_ind, nomass_anc_index)
mass, phi = smf.smf(subh.mass[has_ancestor])
sub.plot(mass,
phi,
lw=2,
ls='--',
c=pretty_colors[i_snap],
label=nomass_label)
del subh
anc_mass, anc_phi = smf.smf(ancestor_mass)
sub.plot(anc_mass, anc_phi, lw=2, c='k', label='Initial Redshift')
#print 'With descendants at snapshot 1', len(ancestor_mass[has_descendant])
#anc_massive = np.where(ancestor_mass[has_descendant] > 0.)
#print 'With mass greater than 0', len(ancestor_mass[has_descendant[anc_massive]])
#anc_mass, anc_phi = smf.smf(ancestor_mass[has_descendant])
#sub.plot(anc_mass, anc_phi, ls='--', lw=4, c='k')
#anc_mass, anc_phi = smf.smf(ancestor_mass[has_descendant[anc_massive]])
#sub.plot(anc_mass, anc_phi, ls='--', lw=2, c='green')
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([6.0, 12.0])
sub.legend(loc='upper right')
if nomass:
nomass_str = '.ancestor_Mstar0'
else:
nomass_str = ''
fig_file = ''.join([
'figure/test/'
'DescendantSubhaloSMF', '.', type, '_subhalo', '.scatter',
str(round(scatter, 2)), '.ancestor',
str(nsnap_ancestor), '.', source, nomass_str, '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def DescendantQAplot(nsnap_descendants, **sfinh_kwargs):
''' The ultimate QAplot t rule them all. 4 panels showing all the properties.
'''
sfinherit_file = InheritSF_file(nsnap_descendants, **sfinh_kwargs)
bloodline = Lineage(nsnap_ancestor=sfinh_kwargs['nsnap_ancestor'],
subhalo_prop=sfinh_kwargs['subhalo_prop'])
if not isinstance(nsnap_descendants, list):
nsnap_descendants = [nsnap_descendants]
bloodline.Read(nsnap_descendants, filename=sfinherit_file)
for nsnap_descendant in nsnap_descendants:
descendant = getattr(bloodline,
'descendant_snapshot' + str(nsnap_descendant))
descendant.sfr_prop = sfinh_kwargs['sfr_prop']
started_here = np.where(
descendant.nsnap_genesis == sfinh_kwargs['nsnap_ancestor'])
start_mass = descendant.mass_genesis[started_here]
# Mass bins
mass_bins = np.arange(7., 12.5, 0.5)
mass_bin_low = mass_bins[:-1]
mass_bin_high = mass_bins[1:]
plt.close()
prettyplot()
fig = plt.figure(1, figsize=[25, 6])
for i_sub in range(1, 5):
sub_i = fig.add_subplot(1, 4, i_sub)
if i_sub == 1: # SMF
mf = SMF()
mass, phi = mf.Obj(descendant)
sub_i.plot(mass,
phi,
lw=4,
c=pretty_colors[descendant.nsnap],
label=r'Simulated')
censub = CentralSubhalos()
censub.Read(descendant.nsnap,
scatter=sfinh_kwargs['subhalo_prop']['scatter'],
source=sfinh_kwargs['subhalo_prop']['source'],
nsnap_ancestor=sfinh_kwargs['nsnap_ancestor'])
mass, phi = mf._smf(censub.mass)
sub_i.plot(mass,
phi,
c='k',
lw=4,
ls='--',
label='Central Subhalos')
sub_i.set_ylim([10**-5, 10**-1])
sub_i.set_xlim([7.5, 12.0])
plt.xticks([8., 9., 10., 11., 12.])
sub_i.set_yscale('log')
# x,y labels
sub_i.set_xlabel(r'Mass $\mathtt{M_*}$', fontsize=25)
sub_i.set_ylabel(r'Stellar Mass Function $\mathtt{\Phi}$',
fontsize=25)
sub_i.legend(loc='upper right', frameon=False)
elif i_sub == 2: # SFMS
# SMF composition based on their initial mass at nsnap_ancestor
for i_m in range(len(mass_bin_low)):
mbin = np.where((start_mass > mass_bin_low[i_m])
& (start_mass <= mass_bin_high[i_m]))
sub_i.scatter(descendant.mass[started_here[0][mbin]],
descendant.sfr[started_here[0][mbin]],
color=pretty_colors[i_m],
label=r"$\mathtt{M_{*,i}=}$" +
str(round(mass_bin_low[i_m], 2)) + "-" +
str(round(mass_bin_high[i_m], 2)))
qfrac = Fq()
m_arr = np.arange(9.0, 12.5, 0.5)
sub_i.plot(m_arr,
qfrac.SFRcut(
m_arr,
descendant.zsnap,
sfms_prop=(sfinh_kwargs['sfr_prop'])['sfms']),
c='k',
ls='--',
lw=4)
sub_i.set_xlim([9.0, 12.0])
sub_i.set_ylim([-5.0, 2.0])
sub_i.set_xlabel(r'$\mathtt{log\;M_*}$', fontsize=25)
sub_i.set_ylabel(r'$\mathtt{log\;SFR}$', fontsize=25)
elif i_sub == 3: #SMHM
for i_m in range(len(mass_bin_low)):
mbin = np.where((start_mass > mass_bin_low[i_m])
& (start_mass <= mass_bin_high[i_m]))
sub_i.scatter(descendant.halo_mass[started_here[0][mbin]],
descendant.mass[started_here[0][mbin]],
color=pretty_colors[i_m],
label=r"$\mathtt{M_{*,i}=}$" +
str(round(mass_bin_low[i_m], 2)) + "-" +
str(round(mass_bin_high[i_m], 2)))
stellarmass = descendant.mass[started_here]
halomass = descendant.halo_mass[started_here]
mbin = np.arange(halomass.min(), halomass.max(), 0.25)
mlow = mbin[:-1]
mhigh = mbin[1:]
muMstar = np.zeros(len(mlow))
sigMstar = np.zeros(len(mlow))
for im in range(len(mlow)):
mbin = np.where((halomass > mlow[im])
& (halomass <= mhigh[im]))
muMstar[im] = np.mean(stellarmass[mbin])
sigMstar[im] = np.std(stellarmass[mbin])
sub_i.errorbar(0.5 * (mlow + mhigh),
muMstar,
yerr=sigMstar,
color='k',
lw=3,
fmt='o',
capthick=2)
sub_i.set_ylim([9.0, 12.0])
sub_i.set_xlim([10.0, 15.0])
sub_i.set_ylabel(r'Stellar Mass $\mathtt{M_*}$', fontsize=25)
sub_i.set_xlabel(r'Halo Mass $\mathtt{M_{Halo}}$', fontsize=25)
#sub_i.legend(loc='upper left', frameon=False, scatterpoints=1)
elif i_sub == 4: # Fq
#mass, fq = descendant.Fq()
sfq = qfrac.Classify(descendant.mass,
descendant.sfr,
descendant.zsnap,
sfms_prop=descendant.sfr_prop['sfms'])
gc = GroupCat(Mrcut=18, position='central')
gc.Read()
gc_sfq = qfrac.Classify(gc.mass,
gc.sfr,
np.mean(gc.z),
sfms_prop=descendant.sfr_prop['sfms'])
#sub_i.plot(mass, fq, color=pretty_colors[descendant.nsnap], lw=3, ls='--',
# label=r'$\mathtt{z =} '+str(descendant.zsnap)+'$')
M_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_low = M_bin[:-1]
M_high = M_bin[1:]
M_mid = 0.5 * (M_low + M_high)
fq = np.zeros(len(M_low))
gc_fq = np.zeros(len(M_low))
for i_m in xrange(len(M_low)):
mlim = np.where((descendant.mass > M_low[i_m])
& (descendant.mass <= M_high[i_m]))
gc_mlim = np.where((gc.mass > M_low[i_m])
& (gc.mass <= M_high[i_m]))
ngal = np.float(len(mlim[0]))
gc_ngal = np.float(len(gc_mlim[0]))
if ngal != 0: # no galaxy in mass bin
ngal_q = np.float(
len(np.where(sfq[mlim] == 'quiescent')[0]))
fq[i_m] = ngal_q / ngal
if gc_ngal != 0:
gc_ngal_q = np.float(
len(np.where(gc_sfq[gc_mlim] == 'quiescent')[0]))
gc_fq[i_m] = gc_ngal_q / gc_ngal
sub_i.plot(M_mid,
fq,
color=pretty_colors[descendant.nsnap],
lw=3,
label=r'$\mathtt{z =} ' + str(descendant.zsnap) +
'$')
fq_model = qfrac.model(
M_bin,
descendant.zsnap,
lit=sfinh_kwargs['sfr_prop']['fq']['name'])
sub_i.plot(M_bin,
fq_model,
color='k',
lw=4,
ls='--',
label=sfinh_kwargs['sfr_prop']['fq']['name'])
sub_i.scatter(M_mid,
gc_fq,
color='k',
s=100,
lw=0,
label='Group Catalog')
sub_i.set_xlim([9.0, 12.0])
sub_i.set_ylim([0.0, 1.0])
sub_i.set_xlabel(r'Mass $\mathtt{M_*}$')
sub_i.set_ylabel(r'Quiescent Fraction $\mathtt{f_Q}$',
fontsize=20)
sub_i.legend(loc='upper left',
frameon=False,
scatterpoints=1,
markerscale=0.75)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig_name = ''.join([
'figure/test/', 'QAplot.', '.nsnap',
str(nsnap_descendant), '.'.join(
(sfinherit_file.rsplit('/')[-1]).rsplit('.')[:-1]), '.png'
])
fig.savefig(fig_name, bbox_inches='tight')
plt.close()
return None
def ImportSubhalo(self, nsnap, subhalo_prop=None):
''' Import Central Subhalo Snapshots with SHAM M* through the CentralSubhalos class
object. The CentralSubhalos class is a wrapper for Andrew Wetzel's TreePM code.
Parameters
----------
nsnap : int
Subhalo Snapshot number. Each snapshot represents a different redshift.
Higher number are at higher redshifts.
subhalo_prop : dict (optional)
Optional dictionary that specifies the subhalo snapshot properties.
The properties include,
- scatter : float
Scatter between SMF and HMF in SHAM
- source : str
Specifies which analytic SMF to use for SHAM
Notes
-----
Also imports snapshot, redshift and t_cosmic metadata.
* SHAM stellar mass
* parent index
* child index
* halo mass
'''
if self.subhalo_prop is None:
self.subhalo_prop = subhalo_prop
else:
if subhalo_prop and self.subhalo_prop != subhalo_prop:
# subhalo_prop values do not match
raise ValueError
centsub = CentralSubhalos()
centsub.Read(
nsnap, # snapshot
scatter=self.subhalo_prop['scatter'], # scatter
source=self.subhalo_prop['source'], # SMF source
nsnap_ancestor=self.subhalo_prop['nsnap_ancestor'])
#print centsub.file_name
# pass CentralSubhalos class attributes to
self.data_columns = self.get_datacol()
for col in centsub.data_columns:
if col == 'halo.m.max':
newcol = 'halo_mass_max'
elif col == 'index':
newcol = 'snap_index'
elif col == 'halo.m':
newcol = 'halo_mass'
else:
newcol = col
setattr(self, newcol, getattr(centsub, col))
if newcol not in self.data_columns:
self.data_columns.append(newcol)
# Meta data
self.metadata = [
'nsnap', 'zsnap', 't_cosmic', 't_step', 'subhalo_prop'
]
# get snapshot redshift/cosmic time data using Andrew's table
n_snaps, z_snap, t_snap, t_wid = np.loadtxt(Util.snapshottable(),
unpack=True,
usecols=[0, 2, 3, 4])
self.nsnap = nsnap
self.zsnap = z_snap[(
n_snaps.tolist()).index(nsnap)] # redshift of snapshot
self.t_cosmic = t_snap[(
n_snaps.tolist()).index(nsnap)] # t_cosmic of snapshot
self.t_step = t_wid[(
n_snaps.tolist()).index(nsnap)] # Gyrs until next snapshot
#print "import_treepm takes ", time.time() - start_time
return None
def QAplot(descendant, sfinh_kwargs, fig_name=None, **kwargs):
''' Given galpop object and the SF Inheritance parametes,
plot its SMF,
'''
# Mass bins
mass_bins = np.arange(7., 12.5, 0.5)
mass_bin_low = mass_bins[:-1]
mass_bin_high = mass_bins[1:]
plt.close()
prettyplot()
fig = plt.figure(1, figsize=[20,12])
for i_sub in range(1,6):
sub_i = fig.add_subplot(2,3,i_sub)
if i_sub == 1: # SMF
mf = SMF()
mass, phi = mf.Obj(descendant)
sub_i.plot(mass, phi, lw=4, c='r', label=r'Simulated')
censub = CentralSubhalos()
censub.Read(descendant.nsnap,
scatter=sfinh_kwargs['subhalo_prop']['scatter'],
source=sfinh_kwargs['subhalo_prop']['source'],
nsnap_ancestor=sfinh_kwargs['nsnap_ancestor'])
mass, phi = mf._smf(censub.mass)
sub_i.plot(mass, phi, c='k', lw=4, ls='--', label='Central Subhalos')
sub_i.set_ylim([10**-5, 10**-1])
sub_i.set_xlim([7.5, 12.0])
plt.xticks([8., 9., 10., 11., 12.])
sub_i.set_yscale('log')
# x,y labels
sub_i.set_ylabel(r'Stellar Mass ($\mathtt{log\; M_*}$)', fontsize=20)
sub_i.set_ylabel(r'Stellar Mass Function $\mathtt{\Phi}$', fontsize=20)
sub_i.legend(loc='upper right', frameon=False)
elif i_sub == 2: # SFMS
# SMF composition based on their initial mass at nsnap_ancestor
# random subsample
n_tot = len(descendant.mass)
r_subsample = np.random.choice(n_tot, size=100000)
sub_i.scatter(descendant.mass[r_subsample], descendant.sfr[r_subsample], color='b', s=0.5)
qfrac = Fq()
m_arr = np.arange(9.0, 12.5, 0.5)
sub_i.plot(
m_arr,
qfrac.SFRcut(m_arr, descendant.zsnap, sfms_prop=(sfinh_kwargs['sfr_prop'])['sfms']),
c='k', ls='--', lw=4)
sub_i.text(10.5, -4., r'$\mathtt{z='+str(descendant.zsnap)+'}$', fontsize=25)
sub_i.set_xlim([9.0, 12.0])
sub_i.set_ylim([-5.0, 2.0])
sub_i.set_ylabel(r'Stellar Mass ($\mathtt{log\; M_*}$)', fontsize=20)
sub_i.set_ylabel(r'$\mathtt{log\;SFR}$', fontsize=20)
elif i_sub == 3: #SMHM
#corner.hist2d(descendant.halo_mass, descendant.mass, ax=sub_i, c='b', fill_contours=True, smooth=1.0)
n_tot = len(descendant.mass)
r_subsample = np.random.choice(n_tot, size=100000)
sub_i.scatter(descendant.halo_mass[r_subsample], descendant.mass[r_subsample], color='b', s=0.5)
stellarmass = descendant.mass
halomass = descendant.halo_mass
mbin = np.arange(halomass.min(), halomass.max(), 0.25)
mlow = mbin[:-1]
mhigh = mbin[1:]
muMstar = np.zeros(len(mlow))
sigMstar = np.zeros(len(mlow))
for im in range(len(mlow)):
mbin = np.where((halomass > mlow[im]) & (halomass <= mhigh[im]))
muMstar[im] = np.mean(stellarmass[mbin])
sigMstar[im] = np.std(stellarmass[mbin])
sub_i.errorbar(0.5*(mlow+mhigh), muMstar, yerr=sigMstar, color='k', lw=3, fmt='o', capthick=2)
sub_i.set_ylim([9.0, 12.0])
sub_i.set_xlim([10.0, 15.0])
sub_i.set_ylabel(r'Stellar Mass ($\mathtt{log\; M_*}$)', fontsize=20)
sub_i.set_xlabel(r'Halo Mass ($\mathtt{log\;M_{Halo}}$)', fontsize=20)
elif i_sub == 4: # Fq
sfq = qfrac.Classify(descendant.mass, descendant.sfr, descendant.zsnap,
sfms_prop=descendant.sfr_prop['sfms'])
gc = GroupCat(Mrcut=18, position='central')
gc.Read()
gc_sfq = qfrac.Classify(gc.mass, gc.sfr, np.mean(gc.z),
sfms_prop=descendant.sfr_prop['sfms'])
#sub_i.plot(mass, fq, color=pretty_colors[descendant.nsnap], lw=3, ls='--',
# label=r'$\mathtt{z =} '+str(descendant.zsnap)+'$')
M_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_low = M_bin[:-1]
M_high = M_bin[1:]
M_mid = 0.5 * (M_low + M_high)
fq = np.zeros(len(M_low))
gc_fq = np.zeros(len(M_low))
for i_m in xrange(len(M_low)):
mlim = np.where((descendant.mass > M_low[i_m]) & (descendant.mass <= M_high[i_m]))
gc_mlim = np.where((gc.mass > M_low[i_m]) & (gc.mass <= M_high[i_m]))
ngal = np.float(len(mlim[0]))
gc_ngal = np.float(len(gc_mlim[0]))
if ngal != 0: # no galaxy in mass bin
ngal_q = np.float(len(np.where(sfq[mlim] == 'quiescent')[0]))
fq[i_m] = ngal_q/ngal
if gc_ngal != 0:
gc_ngal_q = np.float(len(np.where(gc_sfq[gc_mlim] == 'quiescent')[0]))
gc_fq[i_m] = gc_ngal_q/gc_ngal
sub_i.plot(M_mid, fq, color='r', lw=3, label=r'Simulated')
# fQ of the Group Catalog
sub_i.scatter(M_mid, gc_fq, color='k', s=100, lw=0, label='Group Catalog')
# fQ of the model (input) fQ
fq_model = qfrac.model(M_bin, descendant.zsnap, lit=sfinh_kwargs['sfr_prop']['fq']['name'])
sub_i.plot(M_bin, fq_model,
color='k', lw=4, ls='--',
label=sfinh_kwargs['sfr_prop']['fq']['name'].replace('_', ' ').title()
)
sub_i.set_xlim([9.0, 12.0])
sub_i.set_ylim([0.0, 1.0])
sub_i.set_ylabel(r'Stellar Mass ($\mathtt{log\; M_*}$)', fontsize=20)
sub_i.set_ylabel(r'Quiescent Fraction $\mathtt{f_Q}$', fontsize=20)
sub_i.legend(loc='upper left', frameon=False, scatterpoints=1, markerscale=0.75)
elif i_sub == 5: # Tau_Q(M*)
mass_bin = np.arange(9.0, 12.0, 0.1)
tau_m = getTauQ(mass_bin, tau_prop=sfinh_kwargs['evol_prop']['tau'])
sub_i.plot(10**mass_bin, tau_m, color='r', lw=4, label='Simulated')
if 'taus' in kwargs:
tau_slopes, tau_offsets = kwargs['taus']
i_tau_ms = np.zeros((len(mass_bin), len(tau_slopes)))
for i_tau in range(len(tau_slopes)):
i_tau_prop = sfinh_kwargs['evol_prop']['tau'].copy()
i_tau_prop['slope'] = tau_slopes[i_tau]
i_tau_prop['yint'] = tau_offsets[i_tau]
i_tau_m = getTauQ(mass_bin, tau_prop=i_tau_prop)
#sub_i.plot(10**mass_bin, i_tau_m, color='r', alpha=0.1, lw=2)
i_tau_ms[:,i_tau] = i_tau_m
sig_tau = np.zeros(len(mass_bin))
for im in range(len(mass_bin)):
sig_tau[im] = np.std(i_tau_ms[im,:])
sub_i.errorbar(10**mass_bin, tau_m, yerr=sig_tau, color='r', lw=4)
# satellite quenching fraction for comparison
tau_sat = getTauQ(mass_bin, tau_prop={'name': 'satellite'})
sub_i.plot(10**mass_bin, tau_sat, color='black', lw=4, ls='--', label=r'Satellite (Wetzel+ 2013)')
sub_i.set_xlim([10**9.5, 10**11.75])
sub_i.set_ylim([0.0, 2.0])
sub_i.set_xscale('log')
sub_i.set_xlabel(r'Stellar Mass $[\mathtt{M_\odot}]$',fontsize=20)
sub_i.legend(loc='upper right')
sub_i.set_ylabel(r'Quenching Timescales $(\tau_\mathtt{Q})$ [Gyr]',fontsize=20)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
if fig_name is None:
plt.show()
plt.close()
else:
fig.savefig(fig_name, bbox_inches='tight')
plt.close()
return None