def model(self, redshift, source='li-drory-march', **kwargs):
''' Plot the model (Analytic) SMF at given redshift
'''
if 'label' in kwargs:
smf_label = kwargs['label']
kwargs.pop('label', None)
else:
smf_label = 'Analytic'
if 'line_color' in kwargs:
line_color = kwargs['line_color']
kwargs.pop('line_color', None)
else:
line_color = 'black'
if 'line_style' in kwargs:
line_style = kwargs['line_style']
kwargs.pop('line_style', None)
else:
line_style = '--'
if 'lw' in kwargs:
line_width = kwargs['lw']
kwargs.pop('lw', None)
else:
line_width = 3
smf = SMF()
mass, phi = smf.analytic(redshift, source=source)
print phi[np.where((mass > 7.0) & (mass < 7.5))]
self.sub.plot(mass, phi,
c=line_color, ls=line_style, lw=line_width,
label=smf_label
)
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 LineageFinalDescendantSMF(nsnap_ancestor,
subhalo_prop={
'scatter': 0.0,
'source': 'li-march'
},
sfr_prop={
'fq': {
'name': 'wetzelsmooth'
},
'sfr': {
'name': 'average'
}
}):
''' Plot SMF of final descendant. Also mark the composition of the final SMF from Subhalos
that gain stellar mass at different snapshots in the middle of the simulation.
'''
prettyplot()
pretty_colors = prettycolors()
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor,
subhalo_prop=subhalo_prop)
bloodline.Read([1], sfr_prop=sfr_prop)
final_desc = bloodline.descendant_snapshot1
mf = SMF()
smf_plot = plots.PlotSMF()
smf_plot.cgpop(final_desc, line_color='k')
for isnap in range(2, nsnap_ancestor + 1)[::-1]:
started_here = np.where(final_desc.nsnap_genesis == isnap)
mass, phi = mf._smf(final_desc.mass[started_here])
try:
phi_tot += phi
smf_plot.sub.fill_between(mass,
phi_tot - phi,
phi_tot,
color=pretty_colors[isnap],
label='Nsnap=' + str(isnap))
except UnboundLocalError:
phi_tot = phi
smf_plot.sub.fill_between(mass,
np.zeros(len(phi)),
phi,
color=pretty_colors[isnap],
label='Nsnap=' + str(isnap))
smf_plot.set_axes()
fig_file = ''.join([
'figure/test/', 'LineageFinalDescendantSMF', '.ancestor',
str(nsnap_ancestor),
bloodline._file_spec(subhalo_prop=subhalo_prop, sfr_prop=sfr_prop),
'.png'
])
smf_plot.save_fig(fig_file)
return None
def SMF(self, **smf_kwargs):
'''
Calculate the Stellar Mass Function of Cenque Object
'''
if self.mass is None:
raise ValueError
smf = SMF()
return smf.Obj(self, **smf_kwargs)
def SubhaloLF(scatter=0.0, source='cool_ages', nsnap_ancestor=20):
''' Test the Subhalos/CentralSubhalos imported from TreePM by
comparing their measured SMFs to the analytic SMFs
Parameters
----------
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 [11, 7, 4, 1]:
smf = SMF()
subh = Subhalos()
subh.Read(i_snap,
scatter=scatter,
source=source,
nsnap_ancestor=nsnap_ancestor)
subh.mag_r *= -1.
mass, phi = smf.Obj(subh,
dlogm=0.1,
m_arr=np.arange(-24.0, -16., 0.1),
LF=True)
sub.plot(mass, phi, lw=2, ls='-', c=pretty_colors[i_snap % 19])
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)) + "$")
sub.set_yscale('log')
sub.set_ylim([10**-7, 10**-1])
sub.set_xlim([-24.5, -17.8])
sub.legend(loc='upper right')
fig_file = ''.join([
'figure/test/'
'SubhaloLF', '.scatter',
str(round(scatter, 2)), '.ancestor',
str(nsnap_ancestor), '.', source, '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def AllSubhalosSMF(scatter=0.2, source='li-march', age_indicator=None):
''' Compare the SMF of the AllSubhalo object to analytic SMF
'''
# import AllSubhalo object
owl = AllSubhalos(scatter=scatter, source=source)
owl.Build(age_indicator=age_indicator)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
smf = SMF()
# AllSubhalo SMF
owl.mass = getattr(owl, 'm.star')
subh_mass, subh_phi = smf.Obj(owl, dlogm=0.1, LF=False)
sub.plot(subh_mass, subh_phi, lw=4, c=pretty_colors[3])
subh_mass, subh_phi = smf._smf(owl.mass[np.where(owl.sfr != -999.)], dlogm=0.1)
sub.plot(subh_mass, subh_phi, lw=1, c='k')
sub.vlines(9.7, subh_phi.min(), subh_phi.max(), linestyle='--', color='k')
# Analytic SMF
analytic_mass, analytic_phi = smf.analytic(0.05, source=source)
sub.plot(analytic_mass, analytic_phi, lw=4, ls='--', c='k', label='Analytic')
# y-axis
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_ylabel(r'$\Phi$', fontsize=25)
# x-axis
sub.set_xlim([6.0, 12.0])
sub.set_xlabel(r'$\mathtt{M_*}$', fontsize=25)
sub.legend(loc='lower left')
if age_indicator is None:
age_str = 'NOagematch'
else:
age_str = 'agematch_'+age_indicator
fig_file = ''.join(['figure/test/',
'AllSubhalosSMF',
'.sham',
'.', str(scatter),
'.', source,
'.', age_str,
'.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def SubhaloLF(scatter=0.0, source='cool_ages',
nsnap_ancestor=20 ):
''' Test the Subhalos/CentralSubhalos imported from TreePM by
comparing their measured SMFs to the analytic SMFs
Parameters
----------
scatter : float
Float that specifies the scatter in the SMHM relation, which
affects the SHAM masses
source : string
String that specifies which SMF is used for SHAM
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
for i_snap in [11, 7, 4, 1]:
smf = SMF()
subh = Subhalos()
subh.Read(i_snap, scatter=scatter, source=source, nsnap_ancestor=nsnap_ancestor)
subh.mag_r *= -1.
mass, phi = smf.Obj(subh, dlogm=0.1, m_arr=np.arange(-24.0, -16., 0.1), LF=True)
sub.plot(mass, phi, lw=2, ls='-', c=pretty_colors[i_snap % 19])
analytic_mass, analytic_phi = smf.analytic(get_z_nsnap(i_snap), source=source)
sub.plot(analytic_mass, analytic_phi, lw=4, ls='--', c=pretty_colors[i_snap % 19],
label=r"$ z = "+str(round(get_z_nsnap(i_snap),2))+"$")
sub.set_yscale('log')
sub.set_ylim([10**-7, 10**-1])
sub.set_xlim([-24.5, -17.8])
sub.legend(loc='upper right')
fig_file = ''.join(['figure/test/'
'SubhaloLF',
'.scatter', str(round(scatter,2)),
'.ancestor', str(nsnap_ancestor),
'.', source,
'.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def LineageFinalDescendantSMF(nsnap_ancestor,
subhalo_prop={'scatter': 0.0, 'source': 'li-march'},
sfr_prop={'fq': {'name': 'wetzelsmooth'}, 'sfr': {'name': 'average'}}):
''' Plot SMF of final descendant. Also mark the composition of the final SMF from Subhalos
that gain stellar mass at different snapshots in the middle of the simulation.
'''
prettyplot()
pretty_colors=prettycolors()
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor, subhalo_prop=subhalo_prop)
bloodline.Read([1], sfr_prop=sfr_prop)
final_desc = bloodline.descendant_snapshot1
mf = SMF()
smf_plot = plots.PlotSMF()
smf_plot.cgpop(final_desc, line_color='k')
for isnap in range(2, nsnap_ancestor+1)[::-1]:
started_here = np.where(final_desc.nsnap_genesis == isnap)
mass, phi = mf._smf(final_desc.mass[started_here])
try:
phi_tot += phi
smf_plot.sub.fill_between(mass, phi_tot - phi, phi_tot, color=pretty_colors[isnap], label='Nsnap='+str(isnap))
except UnboundLocalError:
phi_tot = phi
smf_plot.sub.fill_between(mass, np.zeros(len(phi)), phi, color=pretty_colors[isnap], label='Nsnap='+str(isnap))
smf_plot.set_axes()
fig_file = ''.join([
'figure/test/',
'LineageFinalDescendantSMF',
'.ancestor', str(nsnap_ancestor),
bloodline._file_spec(subhalo_prop=subhalo_prop, sfr_prop=sfr_prop),
'.png'])
smf_plot.save_fig(fig_file)
return None
def plot(self, mass=None, smf=None, **kwargs):
''' Plot Stellar Mass Function
'''
if 'label' in kwargs:
smf_label = kwargs['label']
kwargs.pop('label', None)
else:
smf_label = ''
if 'line_color' in kwargs:
if isinstance(kwargs['line_color'], str):
line_color = kwargs['line_color']
elif isinstance(kwargs['line_color'], int):
line_color = self.pretty_colors[kwargs['line_color']]
kwargs.pop('line_color', None)
else:
line_color = 'black'
if 'line_style' in kwargs:
line_style = kwargs['line_style']
kwargs.pop('line_style', None)
else:
line_style = '-'
if 'lw' in kwargs:
line_width = kwargs['lw']
kwargs.pop('lw', None)
else:
line_width = 4
if smf is None:
mf = SMF()
masses, phi = mf._smf(mass, **kwargs)
else:
masses, phi = smf
self.sub.plot(masses, phi,
c=line_color, ls=line_style, lw=line_width,
label=smf_label)
return mass, phi
def DescendantSMF_composition(nsnap_descendant, mass_evol=True, nsnap_ancestor=20, abc_step=29,
subhalo_prop = {'scatter': 0.2, 'source': 'li-march'},
sfr_prop = {'fq': {'name': 'wetzelsmooth'}, 'sfr': {'name': 'average'}},
evol_prop = {'sfr': {'dutycycle': {'name': 'notperiodic'}}, 'mass': {'name': 'sham'}}):
sf_inherit_file = InheritSF_file(nsnap_descendant, nsnap_ancestor=nsnap_ancestor,
abc_step=abc_step, subhalo_prop=subhalo_prop, sfr_prop=sfr_prop, evol_prop=evol_prop)
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor, subhalo_prop=subhalo_prop)
bloodline.Read([nsnap_descendant], filename=sf_inherit_file)
descendant = getattr(bloodline, 'descendant_snapshot'+str(nsnap_descendant))
smf_plot = descendant.plotSMF()
mf = SMF()
if mass_evol:
# SMF composition based on their initial mass at nsnap_ancestor
started_here = np.where(descendant.nsnap_genesis == nsnap_ancestor)
start_mass = descendant.mass_genesis[started_here]
mass_bins = np.arange(7., 12.5, 0.5)
mass_bin_low = mass_bins[:-1]
mass_bin_high = mass_bins[1:]
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])
)
mass, phi = mf._smf(descendant.mass[started_here[0][mbin]])
try:
phi_tot += phi
smf_plot.sub.fill_between(mass, phi_tot - phi, phi_tot,
color=pretty_colors[i_m],
label=str(round(mass_bin_low[i_m], 2))+'-'+str(round(mass_bin_high[i_m], 2)))
except UnboundLocalError:
phi_tot = phi
smf_plot.sub.fill_between(mass, np.zeros(len(phi)), phi,
color=pretty_colors[i_m],
label=str(round(mass_bin_low[i_m], 2))+'-'+str(round(mass_bin_high[i_m], 2)))
else:
for isnap in range(2, nsnap_ancestor+1)[::-1]:
started_here = np.where(descendant.nsnap_genesis == isnap)
mass, phi = mf._smf(descendant.mass[started_here])
try:
phi_tot += phi
smf_plot.sub.fill_between(mass, phi_tot - phi, phi_tot, color=pretty_colors[isnap], label='Nsnap='+str(isnap))
except UnboundLocalError:
phi_tot = phi
smf_plot.sub.fill_between(mass, np.zeros(len(phi)), phi, color=pretty_colors[isnap], label='Nsnap='+str(isnap))
smf_plot.set_axes()
mass_evol_str = ''
if mass_evol:
mass_evol_str = '.mass_evol'
fig_file = ''.join([
'figure/test/',
''.join((sf_inherit_file.rsplit('/')[-1]).rsplit('.')[:-1]),
'.SMF_composition',
mass_evol_str, '.png'])
smf_plot.save_fig(fig_file)
return None
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 Compare_DescendantSMF_composition(nsnap_descendant, kwarg_list, n_step=29):
''' Compare the M* distribution at z_final of galaxies from same initial
M* bin for different prescriptions of SF evolution. More specifically
this is to see how well the integrated SF stellar masses match the SHAM
stellar masses.
'''
descendants = []
for kwarg in kwarg_list: # import SF inherited Lineages for different set of arguments
sf_inherit_file = InheritSF_file(nsnap_descendant,
abc_step=n_step,
**kwarg)
bloodline = Lineage(nsnap_ancestor=kwarg['nsnap_ancestor'],
subhalo_prop=kwarg['subhalo_prop'])
bloodline.Read([nsnap_descendant], filename=sf_inherit_file)
descendants.append(
getattr(bloodline, 'descendant_snapshot' + str(nsnap_descendant)))
mf = SMF()
mass_bins = np.arange(7., 12.5, 0.5)
mass_bin_low = mass_bins[:-1]
mass_bin_high = mass_bins[1:]
prettyplot()
pretty_colors = prettycolors()
lstyles = ['-', '--', '-.']
for i_m in range(len(mass_bin_low)):
fig = plt.figure(1)
sub = fig.add_subplot(111)
mass_evol_str = ''
for i_desc, descendant in enumerate(descendants):
# SMF composition based on their initial mass at nsnap_ancestor
started_here = np.where(descendant.nsnap_genesis ==
kwarg_list[i_desc]['nsnap_ancestor'])
start_mass = descendant.mass_genesis[started_here]
mbin = np.where((start_mass > mass_bin_low[i_m])
& (start_mass <= mass_bin_high[i_m]))
mass, phi = mf._smf(descendant.mass[started_here[0][mbin]])
sub.plot(mass,
phi,
c=pretty_colors[i_desc + 1],
lw=4,
ls=lstyles[i_desc],
label=(kwarg_list[i_desc])['evol_prop']['mass']['name'] +
' Masses')
mass_evol_str += (kwarg_list[i_desc])['evol_prop']['mass']['name']
sub.set_xlim([7.5, 12.0])
sub.set_ylim([0.0, 0.012])
sub.set_ylabel(r'$\Phi$', fontsize=25)
sub.set_xlabel(r'$\mathtt{log\;M_*}$', fontsize=25)
sub.legend(loc='upper right')
sub.set_title(''.join([
'log M* = ',
str(round(mass_bin_low[i_m], 2)), '-',
str(round(mass_bin_high[i_m], 2)), ' at Snapshot ',
str(kwarg_list[i_desc]['nsnap_ancestor'])
]),
fontsize=25)
fig_file = ''.join([
'figure/test/', 'DescendantSMF_composition', '.massevol_',
mass_evol_str, '.Mbin',
str(round(mass_bin_low[i_m], 2)), '_',
str(round(mass_bin_high[i_m], 2)), '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
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 SMFevol():
''' Compare the Marchesini interpolation to others in the literature
'''
#zrange = [0.9, 0.5, 0.05]
zrange = [0.9]
source_list = ['li-march', 'li-march-extreme']
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(7, 7))
sub = fig.add_subplot(111)
smf = SMF()
for i_s, source in enumerate(source_list):
for iz, z in enumerate(zrange):
mass, phi = smf.analytic(z, source=source) # SMF phi(M*)
phi = np.log10(phi)
# labels for the SMF sources
if source == 'li-march':
source_lbl = 'Marchesini+2009 \&\nLi+2009 (Fiducial)'
elif source == 'li-march-extreme':
#source_lbl = 'Li$+$2009 $\&$ \nextreme evolution'
source_lbl = 'extreme SMF evolution'
if source == 'li-march':
if z == 0.05:
sub.plot(mass, phi, lw=4, color='#b35a00', label=source_lbl)
sub.text(10.675, 0.01, "z", color='#b35a00', fontsize=25)
sub.text(10.8, 0.01, r"$\mathtt{= 0.05}$", color='#b35a00', fontsize=25)
elif z == 0.5:
sub.plot(mass, phi, lw=4, color='#ff8100')
sub.text(10.8, 0.75*0.01, r"$\mathtt{= 0.5}$", color='#ff8100', fontsize=25)
elif z == 0.9:
sub.plot(mass, phi, lw=4, color='#ffa74d')
sub.text(10.8, 0.75*0.75*0.01, r"$\mathtt{= 0.9}$", color='#ffa74d', fontsize=25)
else:
if z == 0.5:
sub.plot(mass, phi, lw=2, ls='-.', c='k', label=source_lbl)
elif z == 0.9:
sub.plot(mass, phi, lw=2, ls='-.', c='k')
for z in [0.9]:#0.75, 0.9, 1.25]:
mass, phi = smf.analytic(z, source='muzzin') # SMF phi(M*)
phi = np.log10(phi)
if z == 0.9:
phi_low = (1.2/16.25+1.)*phi
phi_high = (13.91-1.23)/(13.91)*phi
sub.fill_between(mass, phi_low, phi_high)
else:
sub.plot(mass, phi, lw=6, ls=':', c='k')
#sub.set_ylim([10**-3.75, 10**-1.75])
sub.set_ylim([-3.75, -1.75])
sub.set_xlim([8.8, 11.5])
sub.set_xticks([9.0, 10.0, 11.0])
sub.minorticks_on()
#sub.set_yscale('log')
sub.set_xlabel(r'log($\mathtt{M_*} /\mathtt{M_\odot}$)', fontsize=25)
sub.set_ylabel(r'log($\mathtt{\Phi / Mpc^{-3}\;dex^{-1}}$)', fontsize=25)
sub.legend(loc='lower left', scatteryoffsets=[0.6])
plt.show()
#fig_file = ''.join(['figure/', 'test_SMF_evol.png'])
#fig.savefig(fig_file, bbox_inches='tight', dpi=150)
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 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 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
def Compare_DescendantSMF_composition(nsnap_descendant, kwarg_list, n_step=29):
''' Compare the M* distribution at z_final of galaxies from same initial
M* bin for different prescriptions of SF evolution. More specifically
this is to see how well the integrated SF stellar masses match the SHAM
stellar masses.
'''
descendants = []
for kwarg in kwarg_list: # import SF inherited Lineages for different set of arguments
sf_inherit_file = InheritSF_file(nsnap_descendant, abc_step=n_step,
**kwarg)
bloodline = Lineage(nsnap_ancestor=kwarg['nsnap_ancestor'], subhalo_prop=kwarg['subhalo_prop'])
bloodline.Read([nsnap_descendant], filename=sf_inherit_file)
descendants.append(getattr(bloodline, 'descendant_snapshot'+str(nsnap_descendant)))
mf = SMF()
mass_bins = np.arange(7., 12.5, 0.5)
mass_bin_low = mass_bins[:-1]
mass_bin_high = mass_bins[1:]
prettyplot()
pretty_colors = prettycolors()
lstyles = ['-', '--', '-.']
for i_m in range(len(mass_bin_low)):
fig = plt.figure(1)
sub = fig.add_subplot(111)
mass_evol_str = ''
for i_desc, descendant in enumerate(descendants):
# SMF composition based on their initial mass at nsnap_ancestor
started_here = np.where(descendant.nsnap_genesis == kwarg_list[i_desc]['nsnap_ancestor'])
start_mass = descendant.mass_genesis[started_here]
mbin = np.where(
(start_mass > mass_bin_low[i_m]) &
(start_mass <= mass_bin_high[i_m])
)
mass, phi = mf._smf(descendant.mass[started_here[0][mbin]])
sub.plot(
mass,
phi,
c=pretty_colors[i_desc+1],
lw=4,
ls=lstyles[i_desc],
label=(kwarg_list[i_desc])['evol_prop']['mass']['name']+' Masses'
)
mass_evol_str += (kwarg_list[i_desc])['evol_prop']['mass']['name']
sub.set_xlim([7.5, 12.0])
sub.set_ylim([0.0, 0.012])
sub.set_ylabel(r'$\Phi$', fontsize=25)
sub.set_xlabel(r'$\mathtt{log\;M_*}$', fontsize=25)
sub.legend(loc='upper right')
sub.set_title(''.join([
'log M* = ', str(round(mass_bin_low[i_m],2)), '-', str(round(mass_bin_high[i_m],2)),
' at Snapshot ', str(kwarg_list[i_desc]['nsnap_ancestor'])]), fontsize=25)
fig_file = ''.join([
'figure/test/',
'DescendantSMF_composition',
'.massevol_',
mass_evol_str,
'.Mbin', str(round(mass_bin_low[i_m],2)), '_', str(round(mass_bin_high[i_m],2)),
'.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def DescendantHMF_composition(nsnap_descendant, nsnap_ancestor=20, n_step=29,
subhalo_prop = {'scatter': 0.2, 'source': 'li-march'},
sfr_prop = {'fq': {'name': 'wetzelsmooth'}, 'sfms': {'name': 'linear'}},
evol_prop = {'sfr': {'dutycycle': {'name': 'notperiodic'}}, 'mass': {'name': 'sham'}}):
''' Halo Mass Function composition at z = z_final by initial halo mass at
nsnap_ancestor.
'''
# import SF inherited Lineage
sf_inherit_file = InheritSF_file(
nsnap_descendant,
nsnap_ancestor=nsnap_ancestor,
abc_step=n_step,
subhalo_prop=subhalo_prop,
sfr_prop=sfr_prop,
evol_prop=evol_prop
)
bloodline = Lineage(
nsnap_ancestor=nsnap_ancestor,
subhalo_prop=subhalo_prop
)
bloodline.Read([nsnap_descendant], filename=sf_inherit_file)
# descendant
descendant = getattr(bloodline, 'descendant_snapshot'+str(nsnap_descendant))
smf_plot = PlotSMF()
mf = SMF()
# SMF composition based on their initial mass at nsnap_ancestor
started_here = np.where(descendant.nsnap_genesis == nsnap_ancestor)
start_mass = descendant.halomass_genesis[started_here]
mass_bins = np.arange(10., 17.5, 0.5)
mass_bin_low = mass_bins[:-1]
mass_bin_high = mass_bins[1:]
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])
)
mass, phi = mf._smf(descendant.halo_mass[started_here[0][mbin]],
m_arr= np.arange(10., 17.1, 0.1))
try:
phi_tot += phi
smf_plot.sub.fill_between(mass, phi_tot - phi, phi_tot,
color=pretty_colors[i_m],
label=str(round(mass_bin_low[i_m], 2))+'-'+str(round(mass_bin_high[i_m], 2)))
except UnboundLocalError:
phi_tot = phi
smf_plot.sub.fill_between(mass, np.zeros(len(phi)), phi,
color=pretty_colors[i_m],
label=str(round(mass_bin_low[i_m], 2))+'-'+str(round(mass_bin_high[i_m], 2)))
#smf_plot.set_axes()
smf_plot.sub.set_yscale('log')
smf_plot.sub.set_xlim([10.0, 17.0])
smf_plot.sub.legend(loc='upper right')
mass_evol_str = '.mass_evol'
fig_file = ''.join([
'figure/test/',
''.join((sf_inherit_file.rsplit('/')[-1]).rsplit('.')[:-1]),
'.HMF_composition',
mass_evol_str, '.png'])
smf_plot.save_fig(fig_file)
return None
def DescendantHMF_composition(nsnap_descendant,
nsnap_ancestor=20,
n_step=29,
subhalo_prop={
'scatter': 0.2,
'source': 'li-march'
},
sfr_prop={
'fq': {
'name': 'wetzelsmooth'
},
'sfms': {
'name': 'linear'
}
},
evol_prop={
'sfr': {
'dutycycle': {
'name': 'notperiodic'
}
},
'mass': {
'name': 'sham'
}
}):
''' Halo Mass Function composition at z = z_final by initial halo mass at
nsnap_ancestor.
'''
# import SF inherited Lineage
sf_inherit_file = InheritSF_file(nsnap_descendant,
nsnap_ancestor=nsnap_ancestor,
abc_step=n_step,
subhalo_prop=subhalo_prop,
sfr_prop=sfr_prop,
evol_prop=evol_prop)
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor,
subhalo_prop=subhalo_prop)
bloodline.Read([nsnap_descendant], filename=sf_inherit_file)
# descendant
descendant = getattr(bloodline,
'descendant_snapshot' + str(nsnap_descendant))
smf_plot = PlotSMF()
mf = SMF()
# SMF composition based on their initial mass at nsnap_ancestor
started_here = np.where(descendant.nsnap_genesis == nsnap_ancestor)
start_mass = descendant.halomass_genesis[started_here]
mass_bins = np.arange(10., 17.5, 0.5)
mass_bin_low = mass_bins[:-1]
mass_bin_high = mass_bins[1:]
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]))
mass, phi = mf._smf(descendant.halo_mass[started_here[0][mbin]],
m_arr=np.arange(10., 17.1, 0.1))
try:
phi_tot += phi
smf_plot.sub.fill_between(mass,
phi_tot - phi,
phi_tot,
color=pretty_colors[i_m],
label=str(round(mass_bin_low[i_m], 2)) +
'-' + str(round(mass_bin_high[i_m], 2)))
except UnboundLocalError:
phi_tot = phi
smf_plot.sub.fill_between(mass,
np.zeros(len(phi)),
phi,
color=pretty_colors[i_m],
label=str(round(mass_bin_low[i_m], 2)) +
'-' + str(round(mass_bin_high[i_m], 2)))
#smf_plot.set_axes()
smf_plot.sub.set_yscale('log')
smf_plot.sub.set_xlim([10.0, 17.0])
smf_plot.sub.legend(loc='upper right')
mass_evol_str = '.mass_evol'
fig_file = ''.join([
'figure/test/', ''.join(
(sf_inherit_file.rsplit('/')[-1]).rsplit('.')[:-1]),
'.HMF_composition', mass_evol_str, '.png'
])
smf_plot.save_fig(fig_file)
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 DescendantSMF_composition(nsnap_descendant,
mass_evol=True,
nsnap_ancestor=20,
abc_step=29,
subhalo_prop={
'scatter': 0.2,
'source': 'li-march'
},
sfr_prop={
'fq': {
'name': 'wetzelsmooth'
},
'sfr': {
'name': 'average'
}
},
evol_prop={
'sfr': {
'dutycycle': {
'name': 'notperiodic'
}
},
'mass': {
'name': 'sham'
}
}):
sf_inherit_file = InheritSF_file(nsnap_descendant,
nsnap_ancestor=nsnap_ancestor,
abc_step=abc_step,
subhalo_prop=subhalo_prop,
sfr_prop=sfr_prop,
evol_prop=evol_prop)
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor,
subhalo_prop=subhalo_prop)
bloodline.Read([nsnap_descendant], filename=sf_inherit_file)
descendant = getattr(bloodline,
'descendant_snapshot' + str(nsnap_descendant))
smf_plot = descendant.plotSMF()
mf = SMF()
if mass_evol:
# SMF composition based on their initial mass at nsnap_ancestor
started_here = np.where(descendant.nsnap_genesis == nsnap_ancestor)
start_mass = descendant.mass_genesis[started_here]
mass_bins = np.arange(7., 12.5, 0.5)
mass_bin_low = mass_bins[:-1]
mass_bin_high = mass_bins[1:]
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]))
mass, phi = mf._smf(descendant.mass[started_here[0][mbin]])
try:
phi_tot += phi
smf_plot.sub.fill_between(
mass,
phi_tot - phi,
phi_tot,
color=pretty_colors[i_m],
label=str(round(mass_bin_low[i_m], 2)) + '-' +
str(round(mass_bin_high[i_m], 2)))
except UnboundLocalError:
phi_tot = phi
smf_plot.sub.fill_between(
mass,
np.zeros(len(phi)),
phi,
color=pretty_colors[i_m],
label=str(round(mass_bin_low[i_m], 2)) + '-' +
str(round(mass_bin_high[i_m], 2)))
else:
for isnap in range(2, nsnap_ancestor + 1)[::-1]:
started_here = np.where(descendant.nsnap_genesis == isnap)
mass, phi = mf._smf(descendant.mass[started_here])
try:
phi_tot += phi
smf_plot.sub.fill_between(mass,
phi_tot - phi,
phi_tot,
color=pretty_colors[isnap],
label='Nsnap=' + str(isnap))
except UnboundLocalError:
phi_tot = phi
smf_plot.sub.fill_between(mass,
np.zeros(len(phi)),
phi,
color=pretty_colors[isnap],
label='Nsnap=' + str(isnap))
smf_plot.set_axes()
mass_evol_str = ''
if mass_evol:
mass_evol_str = '.mass_evol'
fig_file = ''.join([
'figure/test/', ''.join(
(sf_inherit_file.rsplit('/')[-1]).rsplit('.')[:-1]),
'.SMF_composition', mass_evol_str, '.png'
])
smf_plot.save_fig(fig_file)
return None
def SMFevol():
''' Compare the Marchesini interpolation to others in the literature
'''
#zrange = [0.9, 0.5, 0.05]
zrange = [0.9]
source_list = ['li-march', 'li-march-extreme']
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(7, 7))
sub = fig.add_subplot(111)
smf = SMF()
for i_s, source in enumerate(source_list):
for iz, z in enumerate(zrange):
mass, phi = smf.analytic(z, source=source) # SMF phi(M*)
phi = np.log10(phi)
# labels for the SMF sources
if source == 'li-march':
source_lbl = 'Marchesini+2009 \&\nLi+2009 (Fiducial)'
elif source == 'li-march-extreme':
#source_lbl = 'Li$+$2009 $\&$ \nextreme evolution'
source_lbl = 'extreme SMF evolution'
if source == 'li-march':
if z == 0.05:
sub.plot(mass,
phi,
lw=4,
color='#b35a00',
label=source_lbl)
sub.text(10.675, 0.01, "z", color='#b35a00', fontsize=25)
sub.text(10.8,
0.01,
r"$\mathtt{= 0.05}$",
color='#b35a00',
fontsize=25)
elif z == 0.5:
sub.plot(mass, phi, lw=4, color='#ff8100')
sub.text(10.8,
0.75 * 0.01,
r"$\mathtt{= 0.5}$",
color='#ff8100',
fontsize=25)
elif z == 0.9:
sub.plot(mass, phi, lw=4, color='#ffa74d')
sub.text(10.8,
0.75 * 0.75 * 0.01,
r"$\mathtt{= 0.9}$",
color='#ffa74d',
fontsize=25)
else:
if z == 0.5:
sub.plot(mass, phi, lw=2, ls='-.', c='k', label=source_lbl)
elif z == 0.9:
sub.plot(mass, phi, lw=2, ls='-.', c='k')
for z in [0.9]: #0.75, 0.9, 1.25]:
mass, phi = smf.analytic(z, source='muzzin') # SMF phi(M*)
phi = np.log10(phi)
if z == 0.9:
phi_low = (1.2 / 16.25 + 1.) * phi
phi_high = (13.91 - 1.23) / (13.91) * phi
sub.fill_between(mass, phi_low, phi_high)
else:
sub.plot(mass, phi, lw=6, ls=':', c='k')
#sub.set_ylim([10**-3.75, 10**-1.75])
sub.set_ylim([-3.75, -1.75])
sub.set_xlim([8.8, 11.5])
sub.set_xticks([9.0, 10.0, 11.0])
sub.minorticks_on()
#sub.set_yscale('log')
sub.set_xlabel(r'log($\mathtt{M_*} /\mathtt{M_\odot}$)', fontsize=25)
sub.set_ylabel(r'log($\mathtt{\Phi / Mpc^{-3}\;dex^{-1}}$)', fontsize=25)
sub.legend(loc='lower left', scatteryoffsets=[0.6])
plt.show()
#fig_file = ''.join(['figure/', 'test_SMF_evol.png'])
#fig.savefig(fig_file, bbox_inches='tight', dpi=150)
return None
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