def DescendantSSFR(nsnap_descendants, **kwargs):
''' 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_descendants, **kwargs)
bloodline = Lineage(nsnap_ancestor=kwargs['nsnap_ancestor'],
subhalo_prop=kwargs['subhalo_prop'])
if not isinstance(nsnap_descendants, list):
nsnap_descendants = [nsnap_descendants]
bloodline.Read(nsnap_descendants, filename=sf_inherit_file)
# descendant
for nsnap_descendant in nsnap_descendants:
descendant = getattr(bloodline,
'descendant_snapshot' + str(nsnap_descendant))
fig_name = ''.join([
'figure/test/', 'SSFR.', '.nsnap',
str(nsnap_descendant), '.'.join(
(sf_inherit_file.rsplit('/')[-1]).rsplit('.')[:-1]), '.png'
])
descendant.plotSsfr(line_color='red',
line_width=4,
sfms_prop=kwargs['sfr_prop']['sfms'],
z=descendant.zsnap,
groupcat=True,
savefig=fig_name)
plt.close()
return None
def LineageAncestorSFMS(nsnap_ancestor,
subhalo_prop={
'scatter': 0.0,
'source': 'li-march'
},
sfr_prop={
'fq': {
'name': 'wetzelsmooth'
},
'sfr': {
'name': 'average'
}
}):
'''Plot the SF-MS of the lineage ancestor object. This is mainly to make sure
that the AssignSFR routine is working properly
'''
# read in lineage
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor,
subhalo_prop=subhalo_prop)
bloodline.Read([1], sfr_prop=sfr_prop)
ancestor = getattr(bloodline, 'ancestor')
sfms_plot = plots.PlotSFMS()
sfms_plot.cenque(ancestor, justsf=True) # lineage ancestor
sfms_plot.param_sfms(nsnap=nsnap_ancestor)
sfms_plot_file = ''.join([
'figure/test/', 'LineageAncestorSFMS',
str(nsnap_ancestor),
bloodline._file_spec(subhalo_prop=subhalo_prop, sfr_prop=sfr_prop),
'.png'
])
sfms_plot.save_fig(sfms_plot_file)
return None
def AncestorPlots(**sfinh_kwargs):
''' FQ for the SF Inherited Ancestor galaxy population. This test is mainly
to see what the discrepancy between the empirical SF/Q classifcation and the
parameterized FQ model. Ideally, the model and the
'''
bloodline = Lineage(nsnap_ancestor=sfinh_kwargs['nsnap_ancestor'],
subhalo_prop=sfinh_kwargs['subhalo_prop'])
bloodline.Read([1])
bloodline.AssignSFR_ancestor(sfr_prop=sfinh_kwargs['sfr_prop'])
ancestor = getattr(bloodline, 'ancestor')
ancestor.sfr_prop = sfinh_kwargs['sfr_prop']
fig_file = lambda prop: ''.join([
'figure/test/', 'Ancestor',
str(sfinh_kwargs['nsnap_ancestor']), '.',
prop.upper(), '.png'
])
ancestor.plotSsfr(savefig=fig_file('ssfr'))
plt.close()
# FQ
ancestor.plotFq(model=True, savefig=fig_file('fq'))
ancestor.plotSFMS(sfqcut=True,
gal_class='all',
bovyplot=False,
sigSFR=False,
model=False,
savefig=fig_file('sfms'))
plt.close()
return None
def _Read_Lineage(self):
''' Read in Lineage object and import all the GalPop objects within
lineage to Inherit object.
'''
# read in the lineage (< 0.05 seconds for one snapshot)
if not self.quiet:
read_time = time.time()
bloodline = Lineage(nsnap_ancestor=self.nsnap_ancestor,
subhalo_prop=self.subhalo_prop,
quiet=self.quiet)
bloodline.Read(self.nsnap_descendants, quiet=self.quiet)
#DEFUNCT
#if 'subhalogrowth' in self.sfr_prop.keys():
# # depending on whether SFR assign includes subhalo growth AM
# sfr_prop['subhalogrowth']['nsnap_descendant'] = nsnap_descendant
# assign SFR to ancestor object
bloodline.AssignSFR_ancestor(sfr_prop=self.sfr_prop, quiet=self.quiet)
if not self.quiet:
print 'Lineage Read Time = ', time.time() - read_time
self.ancestor = bloodline.ancestor # ancestor object
self.ancestor.tQ[np.where(self.ancestor.tQ != 999.)] = 0.
self.MshamEvol = self.ancestor.Msham_evol[0]
self._ImportDescendants(bloodline)
return None
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 DescendantSMHM_composition(nsnap_descendant, abc_step=29, **sfinh_kwargs):
''' Plot the Stellar Mass to Halo Mass relation.
'''
sfinherit_file = InheritSF_file(nsnap_descendant,
abc_step=abc_step,
**sfinh_kwargs)
bloodline = Lineage(nsnap_ancestor=sfinh_kwargs['nsnap_ancestor'],
subhalo_prop=sfinh_kwargs['subhalo_prop'])
bloodline.Read([nsnap_descendant], filename=sfinherit_file)
descendant = getattr(bloodline,
'descendant_snapshot' + str(nsnap_descendant))
smhm_plot = descendant.plotSMHM(bovyplot=False, scatter=False)
# SMF composition based on their initial mass at nsnap_ancestor
started_here = np.where(
descendant.nsnap_genesis == sfinh_kwargs['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]))
smhm_plot.plot(stellarmass=descendant.mass[started_here[0][mbin]],
halomass=descendant.halo_mass[started_here[0][mbin]],
bovyplot=False,
color=i_m,
label=r"$\mathtt{M_{*,i}=}$" +
str(round(mass_bin_low[i_m], 2)) + "-" +
str(round(mass_bin_high[i_m], 2)))
smhm_plot.plotSummary(stellarmass=descendant.mass,
halomass=descendant.halo_mass)
smhm_plot.sub.legend(loc='upper left', scatterpoints=1)
smhm_plot.set_axes()
fig_file = ''.join([
'figure/test/', 'SMHM_composition.', ''.join(
(sfinherit_file.rsplit('/')[-1]).rsplit('.')[:-1]), '.png'
])
smhm_plot.save_fig(fig_file)
return None
def DescendantFQ(nsnap_descendant, abc_step=29, **sfinh_kwargs):
''' FQ for the SF Inherited Descendant galaxy population
'''
sfinherit_file = InheritSF_file(nsnap_descendant,
abc_step=abc_step,
**sfinh_kwargs)
bloodline = Lineage(nsnap_ancestor=sfinh_kwargs['nsnap_ancestor'],
subhalo_prop=sfinh_kwargs['subhalo_prop'])
bloodline.Read([nsnap_descendant], filename=sfinherit_file)
descendant = getattr(bloodline,
'descendant_snapshot' + str(nsnap_descendant))
descendant.sfr_prop = sfinh_kwargs['sfr_prop']
fig_file = ''.join([
'figure/test/', 'Fq.', '.'.join(
(sfinherit_file.rsplit('/')[-1]).rsplit('.')[:-1]), '.png'
])
descendant.plotFq(model=sfinh_kwargs['sfr_prop']['fq']['name'],
savefig=fig_file)
return None
def Read_InheritSF(nsnap_descendant,
nsnap_ancestor=20,
n_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'
}
},
flag=None):
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,
flag=flag)
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor,
subhalo_prop=subhalo_prop)
bloodline.Read([nsnap_descendant], filename=sf_inherit_file)
return bloodline
def InheritSF(nsnap_descendant,
nsnap_ancestor=20,
subhalo_prop=None,
sfr_prop=None,
evol_prop=None,
quiet=True):
''' Evolve star formation properties of 'ancestor' CentralGalaxyPopulation class at
redshift specified by nsnap_ancestor to descendant CentralGalaxlyPopulation object
at redshift specified by nsnap_descendant. Both ancestor and descendant objects
are attributes in the Lineage class, which contains all the 'lineage' information
i.e. all the halo tracking information.
Parameters
----------
nsnap_ancestor : int
Snapshot number of ancestor CGPop object. The ancestor object is constructed
with from subhalo catalog with subhalo_prop properties. They are also assigned
SFRs with sfr_prop properties.
subhalo_prop : dict
Dictionary that describes the subhalo properties. The key 'scatter' corresponds
to the M*-M_halo relation. The key 'soruce' describes the source SMF used for
the SHAM masses.
sfr_prop : dict
Dictionary that describes the SFR properties assigned to the ancestor CenQue object.
The key 'fq' describes the quiescent fraction used for the ancestor while the key
'sfr' describes the properties of the SFR assignment.
evol_prop : dict
Dictionary that consists of dictionaries which each describe paramter choices in
the model.
- evol_prop['pq'] dictates the quenching properties.
- evol_prop['tau'] dictates the quenching timescale.
- evol_prop['sfr'] dictates the SFR evolution.
- evol_prop['mass'] dictates the mass evolution.
'''
if isinstance(nsnap_descendant, list):
d_list = True
else:
d_list = False
# read in the lineage (< 0.05 seconds for one snapshot)
#read_time = time.time()
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor,
subhalo_prop=subhalo_prop,
quiet=quiet)
if not d_list:
bloodline.Read(range(nsnap_descendant, nsnap_ancestor), quiet=quiet)
else:
bloodline.Read(range(np.min(nsnap_descendant), nsnap_ancestor),
quiet=quiet)
if 'subhalogrowth' in sfr_prop.keys(
): # depending on whether SFR assign includes subhalo growth AM
sfr_prop['subhalogrowth']['nsnap_descendant'] = nsnap_descendant
bloodline.AssignSFR_ancestor(sfr_prop=sfr_prop, quiet=quiet)
#print 'Lineage Read Time = ', time.time() - read_time
ancestor = bloodline.ancestor # ancestor object
t_init = ancestor.t_cosmic
z_init = ancestor.zsnap
ancestor.tQ[np.where(ancestor.tQ != 999.)] = 0.
if not isinstance(nsnap_descendant, list):
nsnap_descendant = [nsnap_descendant]
descendants = []
sf_ancestors, q_ancestors = [], []
successions, wills = [], []
for nd in nsnap_descendant:
des = getattr(bloodline, 'descendant_snapshot' + str(nd))
des._clean_initialize() # initialize SF properties
des.sfr_prop = ancestor.sfr_prop
# match indices up with each other
succession, will = intersection_index(
getattr(des, 'ancestor' + str(nsnap_ancestor)),
ancestor.snap_index)
if len(succession) != len(des.mass):
raise ValueError('Something wrong with the lineage')
q_ancestor = np.where(
ancestor.sfr_class[will] == 'quiescent')[0] # Q ancestors
sf_ancestor = np.where(
ancestor.sfr_class[will] == 'star-forming')[0] # SF ancestors
if not quiet:
print "nsnap_descendant = ", nd, "Ancestors: Nq = ", len(
q_ancestor), ', Nsf = ', len(sf_ancestor)
descendants.append(des)
wills.append(will)
successions.append(succession)
q_ancestors.append(q_ancestor)
sf_ancestors.append(sf_ancestor)
# Evolve queiscent ancestor galaxies
if not quiet:
q_time = time.time()
descendants = _QuiescentEvol(ancestor,
descendants,
successions=successions,
wills=wills,
q_ancestors=q_ancestors)
if not quiet:
print 'Quiescent evolution takes ', time.time() - q_time
# Evolve Star Forming Galaxies
if not quiet:
sf_time = time.time()
################## PQ BASED DROPPED ##############################
# if evol_prop['type'] == 'pq_based':
# _StarformingEvol_Pq(ancestor, descendant, succession=succession, will=will,
# evol_prop=evol_prop, sfr_prop=sfr_prop,
# lineage=bloodline)
# elif evol_prop['type'] == 'simult':
################## PQ BASED DROPPED ##############################
descendants = _StarformingEvol_SimulEvo(ancestor,
descendants,
successions=successions,
wills=wills,
evol_prop=evol_prop,
sfr_prop=sfr_prop,
lineage=bloodline,
quiet=quiet)
if not quiet:
print 'Star Forming Evolution takes ', time.time() - sf_time
descendants = _Overquenching(descendants,
successions=successions,
wills=wills,
sf_ancestors=sf_ancestors)
for descendant in descendants:
descendant.data_columns = list(descendant.data_columns) + [
'ssfr', 'sfr', 'min_ssfr', 'sfr_class'
]
setattr(bloodline, 'descendant_snapshot' + str(descendant.nsnap),
descendant)
return bloodline
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 DescendantSFMS_composition(nsnap_descendant,
abc_step=29,
bovyplot=False,
**sfinh_kwargs):
''' SFMS for the SF Inherited Descendant galaxy population
'''
sfinherit_file = InheritSF_file(nsnap_descendant,
abc_step=abc_step,
**sfinh_kwargs)
bloodline = Lineage(nsnap_ancestor=sfinh_kwargs['nsnap_ancestor'],
subhalo_prop=sfinh_kwargs['subhalo_prop'])
bloodline.Read([nsnap_descendant], filename=sfinherit_file)
descendant = getattr(bloodline,
'descendant_snapshot' + str(nsnap_descendant))
if not bovyplot:
sfms_plot = descendant.plotSFMS(bovyplot=False, scatter=False)
# SMF composition based on their initial mass at nsnap_ancestor
started_here = np.where(
descendant.nsnap_genesis == sfinh_kwargs['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]))
sfms_plot.plot(
mass=descendant.mass[started_here[0][mbin]],
sfr=descendant.sfr[started_here[0][mbin]],
sfr_class=descendant.sfr_class[started_here[0][mbin]],
gal_class='quiescent',
bovyplot=False,
sigSFR=False,
color=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)
sfms_plot.sub.plot(m_arr,
qfrac.SFRcut(
m_arr,
descendant.zsnap,
sfms_prop=(sfinh_kwargs['sfr_prop'])['sfms']),
c='k',
ls='--',
lw=4)
bovyplot_str = ''
else:
sfms_plot = descendant.plotSFMS(bovyplot=True)
bovyplot_str = '.bovy'
sfms_plot.sub.legend(loc='lower right', scatterpoints=1)
fig_file = ''.join([
'figure/test/', 'SFMS_composition.', '.'.join(
(sfinherit_file.rsplit('/')[-1]).rsplit('.')[:-1]), bovyplot_str,
'.png'
])
sfms_plot.save_fig(fig_file)
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 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 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 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 InheritSF(nsnap_descendant,
nsnap_ancestor=20,
subhalo_prop={
'scatter': 0.0,
'source': 'li-march'
},
sfr_prop={
'fq': {
'name': 'wetzelsmooth'
},
'sfms': {
'name': 'linear',
'mslope': 0.55,
'zslope': 1.1
}
},
evol_prop={
'pq': {
'slope': 0.05,
'yint': 0.0
},
'tau': {
'name': 'line',
'fid_mass': 10.75,
'slope': -0.6,
'yint': 0.6
},
'sfr': {
'dutycycle': {
'name': 'notperiodic'
}
},
'mass': {
'name': 'sham'
}
}):
''' Evolve star formation properties of ancestor CentralGalaxyPopulation class within
the Lineage Class to descendant CentralGalaxlyPopulation object
Parameters
----------
nsnap_ancestor : int
Snapshot number of ancestor CGPop object. The ancestor object is constructed
with from subhalo catalog with subhalo_prop properties. They are also assigned
SFRs with sfr_prop properties.
subhalo_prop : dict
Dictionary that describes the subhalo properties. The key 'scatter' corresponds
to the M*-M_halo relation. The key 'soruce' describes the source SMF used for
the SHAM masses.
sfr_prop : dict
Dictionary that describes the SFR properties assigned to the ancestor CenQue object.
The key 'fq' describes the quiescent fraction used for the ancestor while the key
'sfr' describes the properties of the SFR assignment.
evol_prop : dict
Dictionary that consists of dictionaries which each describe paramter choices in
the model.
- evol_prop['pq'] dictates the quenching properties.
- evol_prop['tau'] dictates the quenching timescale.
- evol_prop['sfr'] dictates the SFR evolution.
- evol_prop['mass'] dictates the mass evolution.
'''
# make sure that snapshot = 1 is included among imported descendants
# and the first element of the list
if isinstance(nsnap_descendant, list):
raise ValueError('nsnap_descendant arg has to be an int')
# evolution properties
pq_prop = evol_prop['pq']
tau_prop = evol_prop['tau']
sfrevol_prop = evol_prop['sfr']
massevol_prop = evol_prop['mass']
# read in the lineage (< 0.05 seconds for one snapshot)
read_time = time.time()
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor,
subhalo_prop=subhalo_prop)
bloodline.Read(range(nsnap_descendant, nsnap_ancestor), sfr_prop='default')
if 'subhalogrowth' in sfr_prop.keys():
sfr_prop['subhalogrowth']['nsnap_descendant'] = nsnap_descendant
bloodline.AssignSFR_ancestor(sfr_prop=sfr_prop)
print 'Lineage Read Time = ', time.time() - read_time
ancestor = bloodline.ancestor # ancestor object
t_init = ancestor.t_cosmic
z_init = ancestor.zsnap
# descendant object
descendant = getattr(bloodline,
'descendant_snapshot' + str(nsnap_descendant))
t_final = descendant.t_cosmic
z_final = descendant.zsnap
print 'Evolve until z = ', z_final
# initialize SF properties of descendant
n_descendant = len(descendant.snap_index)
descendant.sfr = np.repeat(-999., n_descendant)
descendant.ssfr = np.repeat(-999., n_descendant)
descendant.min_ssfr = np.repeat(-999., n_descendant)
descendant.tau = np.repeat(-999., n_descendant)
descendant.sfr_class = np.chararray(n_descendant, itemsize=16)
descendant.sfr_class[:] = ''
succession, will = intersection_index(
getattr(descendant, 'ancestor' + str(nsnap_ancestor)),
ancestor.snap_index)
if len(succession) != len(descendant.mass):
raise ValueError('Something wrong with the lineage')
q_ancestors = np.where(
ancestor.sfr_class[will] == 'quiescent')[0] # Q ancestors
sf_ancestors = np.where(
ancestor.sfr_class[will] == 'star-forming')[0] # SF ancestors
# Evolve queiscent ancestor galaxies
q_time = time.time()
descendant = _QuiescentEvol(ancestor,
descendant,
succession=succession,
will=will)
print 'Quiescent evolution takes ', time.time() - q_time
# Evolve Star Forming Galaxies
sf_time = time.time()
_StarformingEvol(ancestor,
descendant,
succession=succession,
will=will,
evol_prop=evol_prop,
sfr_prop=sfr_prop,
lineage=bloodline)
print 'Star Forming Evolution takes ', time.time() - sf_time
# Deal with over quenched galaxies
overquenched = np.where(descendant.min_ssfr[succession[sf_ancestors]] >
descendant.ssfr[succession[sf_ancestors]])
if len(overquenched[0]) > 0:
descendant.ssfr[succession[
sf_ancestors[overquenched]]] = descendant.min_ssfr[succession[
sf_ancestors[overquenched]]]
descendant.sfr[succession[sf_ancestors[overquenched]]] = descendant.ssfr[succession[sf_ancestors[overquenched]]] \
+ descendant.mass[succession[sf_ancestors[overquenched]]]
#descendant.tau[succession[sf_ancestors[overquenched]]] = -999.
descendant.data_columns = list(descendant.data_columns) + [
'ssfr', 'sfr', 'min_ssfr', 'sfr_class'
] #, 'tau'])
setattr(bloodline, 'descendant_snapshot' + str(nsnap_descendant),
descendant)
return bloodline