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 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 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