def _StarformingEvol(self):
''' Evolve the star-forming galaxy population. This involves evolving their
stellar mass and star formation rates while quenching some of them to match
the quiescent fraction, fQ(M*, z), simultaneously.
'''
for nsnap_d in self.descendant_dict.keys():
allwill = self.descendant_dict[str(nsnap_d)].will.copy()
allsucc = self.descendant_dict[str(nsnap_d)].succession.copy()
anc_sfr_class = self.ancestor.sfr_class.copy()
q_ancestors = np.where(
anc_sfr_class[allwill] == 'quiescent')[0] # Q ancestors
sf_ancestors = np.where(
anc_sfr_class[allwill] == 'star-forming')[0] # SF ancestors
# star-formation duty cycle parameters (takes a long time ~15 sec)
if not self.quiet:
dutycycle_time = time.time()
self.dutycycle_prop = sfr_evol.dutycycle_param(
len(sf_ancestors),
dutycycle_prop=self.evol_prop['sfr']['dutycycle'])
self.dutycycle_prop['delta_sfr'] = self.ancestor.delta_sfr[
allwill[sf_ancestors]].copy()
if not self.quiet:
print 'SFR dutycycle properties take ', time.time(
) - dutycycle_time, ' to generate'
logsfr, t_quench = self._Evol_MshamSFR(nsnap_d, allwill,
sf_ancestors, q_ancestors)
self.descendant_dict[str(nsnap_d)].sfr[
allsucc[sf_ancestors]] = logsfr
self.descendant_dict[str(nsnap_d)].ssfr[allsucc[sf_ancestors]] = \
self.descendant_dict[str(nsnap_d)].sfr[allsucc[sf_ancestors]] - \
self.descendant_dict[str(nsnap_d)].mass[allsucc[sf_ancestors]]
is_qing = np.where(
t_quench < self.descendant_dict[str(nsnap_d)].t_cosmic)
is_notqing = np.where(
t_quench >= self.descendant_dict[str(nsnap_d)].t_cosmic)
self.descendant_dict[str(nsnap_d)].sfr_class[allsucc[sf_ancestors[is_qing]]] = \
'quiescent'
self.descendant_dict[str(nsnap_d)].sfr_class[allsucc[sf_ancestors[is_notqing]]] = \
'star-forming'
self.descendant_dict[str(nsnap_d)].t_quench[allsucc[
sf_ancestors[is_qing]]] = t_quench[is_qing]
return None
def _StarformingEvol(self):
''' Evolve the star-forming galaxy population. This involves evolving their
stellar mass and star formation rates while quenching some of them to match
the quiescent fraction, fQ(M*, z), simultaneously.
'''
for nsnap_d in self.descendant_dict.keys():
allwill = self.descendant_dict[str(nsnap_d)].will.copy()
allsucc = self.descendant_dict[str(nsnap_d)].succession.copy()
anc_sfr_class = self.ancestor.sfr_class.copy()
q_ancestors = np.where(anc_sfr_class[allwill] == 'quiescent')[0] # Q ancestors
sf_ancestors = np.where(anc_sfr_class[allwill] == 'star-forming')[0] # SF ancestors
# star-formation duty cycle parameters (takes a long time ~15 sec)
if not self.quiet:
dutycycle_time = time.time()
self.dutycycle_prop = sfr_evol.dutycycle_param(
len(sf_ancestors),
dutycycle_prop=self.evol_prop['sfr']['dutycycle'])
self.dutycycle_prop['delta_sfr'] = self.ancestor.delta_sfr[allwill[sf_ancestors]].copy()
if not self.quiet:
print 'SFR dutycycle properties take ', time.time() - dutycycle_time, ' to generate'
logsfr, t_quench = self._Evol_MshamSFR(nsnap_d, allwill, sf_ancestors, q_ancestors)
self.descendant_dict[str(nsnap_d)].sfr[allsucc[sf_ancestors]] = logsfr
self.descendant_dict[str(nsnap_d)].ssfr[allsucc[sf_ancestors]] = \
self.descendant_dict[str(nsnap_d)].sfr[allsucc[sf_ancestors]] - \
self.descendant_dict[str(nsnap_d)].mass[allsucc[sf_ancestors]]
is_qing = np.where(t_quench < self.descendant_dict[str(nsnap_d)].t_cosmic)
is_notqing = np.where(t_quench >= self.descendant_dict[str(nsnap_d)].t_cosmic)
self.descendant_dict[str(nsnap_d)].sfr_class[allsucc[sf_ancestors[is_qing]]] = \
'quiescent'
self.descendant_dict[str(nsnap_d)].sfr_class[allsucc[sf_ancestors[is_notqing]]] = \
'star-forming'
self.descendant_dict[str(nsnap_d)].t_quench[allsucc[sf_ancestors[is_qing]]] = t_quench[is_qing]
return None
def _StarformingEvol_Pq(ancestor,
descendant,
succession=None,
will=None,
sfr_prop=None,
evol_prop=None,
lineage=None):
''' Evolve the stellar mass and star formation rates simultaneously.
The stellar mass evolution is dictated by the SFR.
'''
sfms_prop = sfr_prop['sfms']
pq_prop = evol_prop['pq']
tau_prop = evol_prop['tau']
sfrevol_prop = evol_prop['sfr']
dutycycle_prop = sfrevol_prop['dutycycle']
massevol_prop = evol_prop['mass']
# quenching probability
pq_time = time.time()
P_q = _getPq(ancestor,
descendant,
succession=succession,
will=will,
pq_prop=pq_prop,
sfr_prop=sfr_prop)
print 'Quenching Probabilities takes : ', time.time() - pq_time
# quenching time/redshift
tq_time = time.time()
descendant, t_q, z_q = _tQuench(ancestor,
descendant,
P_q,
succession=succession,
will=will)
print 'tQuench takes: ', time.time() - tq_time
# star forming ancestors
sf_ancestors = np.where(
ancestor.sfr_class[will] == 'star-forming')[0] # SF ancestors
# star-formation duty cycle parameters
dutycycle_time = time.time()
dutycycle_prop = sfr_evol.dutycycle_param(len(sf_ancestors),
dutycycle_prop=dutycycle_prop)
dutycycle_prop['delta_sfr'] = ancestor.delta_sfr[will[sf_ancestors]]
print 'SFR dutycycle properties take ', time.time(
) - dutycycle_time, ' to generate'
M_q = np.repeat(-999., len(sf_ancestors))
# keywords for logSFR(M*,t)
kwargs_sfr = {
't_init': descendant.tsnap_genesis[succession[sf_ancestors]],
't_q': t_q,
'z_q': z_q,
'M_q': M_q,
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'sfms_prop': sfms_prop
}
sham_mass = descendant.mass[
succession[sf_ancestors]].copy() # descendant SHAM masses
if massevol_prop['name'] == 'integrated': # integrated stellar mass
mass_time = time.time()
descendant.mass[succession[sf_ancestors]], descendant.sfr[succession[sf_ancestors]], blah = \
M_integrate(
ancestor.mass_genesis[will[sf_ancestors]],
ancestor.tsnap_genesis[will[sf_ancestors]],
descendant.t_cosmic,
massevol_prop=massevol_prop,
kwargs_sfr=kwargs_sfr # kwargs for logSFR(M*,t) function
)
if np.min(descendant.mass[succession[sf_ancestors]] -
ancestor.mass[will[sf_ancestors]]) < 0.0:
raise ValueError("Integrated mass can't reduce the mass")
print 'Integrated Masses takes ', time.time() - mass_time
elif massevol_prop['name'] == 'sham': # SHAM masses
# determine the quenching stellar mass (the SHAM stellar mass at t_Q)
M_q = _SHAM_Mquenching(kwargs_sfr['t_q'],
lineage=lineage,
descendant=descendant,
descendant_index=succession[sf_ancestors])
kwargs_sfr['M_q'] = M_q
descendant.sfr[succession[sf_ancestors]] = \
logSFR_M_t(sham_mass, descendant.t_cosmic, **kwargs_sfr)
# calculate SSFR based on evloved SFR and M*
descendant.ssfr[succession[sf_ancestors]] = \
descendant.sfr[succession[sf_ancestors]] - descendant.mass[succession[sf_ancestors]]
return
def _StarformingEvol_SimulEvo(ancestor,
descendants,
successions=None,
wills=None,
sfr_prop=None,
evol_prop=None,
lineage=None,
quiet=True):
''' Evolve the stellar mass, star formation rates, and quench galaxies simultaneously.
'''
sfms_prop = sfr_prop['sfms'] # SFMS properties
fq_prop = sfr_prop['fq'] # fq_prop
tau_prop = evol_prop['tau'] # Quenching timescale properties
fudge_prop = evol_prop['fudge']
dutycycle_prop = evol_prop['sfr']['dutycycle'] # SF dutycycle properties
massevol_prop = evol_prop['mass'] # mass evolution properties
if len(descendants) > 1:
allwill = np.array(list(set(list(
np.concatenate(wills))))) # combine will of unique elements
t_output = []
for des in descendants:
t_output.append(des.t_cosmic)
else:
t_output = [descendants[0].t_cosmic]
allwill = wills[0]
sf_ancestors = np.where(
ancestor.sfr_class[allwill] == 'star-forming')[0] # SF ancestors
q_ancestors = np.where(
ancestor.sfr_class[allwill] == 'quiescent')[0] # Q ancestors
# star-formation duty cycle parameters
if not quiet:
dutycycle_time = time.time()
dutycycle_prop = sfr_evol.dutycycle_param(len(sf_ancestors),
dutycycle_prop=dutycycle_prop)
dutycycle_prop['delta_sfr'] = ancestor.delta_sfr[allwill[sf_ancestors]]
if not quiet:
print 'SFR dutycycle properties take ', time.time(
) - dutycycle_time, ' to generate'
MshamEvol = ancestor.Msham_evol[0]
# keywords for logSFR(M*,t)
kwargs_sfr = {
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'fudge_prop': fudge_prop,
'fq_prop': fq_prop,
'sfms_prop': sfms_prop,
'massevol_prop': massevol_prop
}
if not quiet:
mass_time = time.time()
# DEFUNCT FOR NOW. needs to be updated
#if massevol_prop['name'] == 'integrated':
# M_evolved, SFR_evolved, tQ = \
# Evol_IntegMstarSFR(
# ancestor.mass_genesis[will[sf_ancestors]],
# ancestor.tsnap_genesis[will[sf_ancestors]],
# descendant.t_cosmic,
# tQ0=ancestor.tQ[will[sf_ancestors]],
# MQ0=ancestor.MQ[will[sf_ancestors]],
# ancestor_Mq=MshamEvol[will[q_ancestors],:],
# **kwargs_sfr
# )
#elif massevol_prop['name'] == 'sham':
bef_tsnap = ancestor.tsnap_genesis[allwill[sf_ancestors]]
bef_tQ = ancestor.tQ[allwill[sf_ancestors]]
bef_MQ = ancestor.MQ[allwill[sf_ancestors]]
bef_sfr = ancestor.sfr[allwill[sf_ancestors]]
bef_MshamEvol = MshamEvol[allwill]
SFRs_evolved, tQ = \
Evol_shamMstarSFR(
ancestor.tsnap_genesis[allwill[sf_ancestors]],
t_output,
tQ0=ancestor.tQ[allwill[sf_ancestors]],
MQ0=ancestor.MQ[allwill[sf_ancestors]],
SFR0=ancestor.sfr[allwill[sf_ancestors]],
q_Mshams=MshamEvol[allwill[q_ancestors],:],
sf_Mshams=MshamEvol[allwill[sf_ancestors],:],
**kwargs_sfr
)
print 'tsnap stays the same', np.array_equal(
bef_tsnap, ancestor.tsnap_genesis[allwill[sf_ancestors]])
print 'tQ stays the same', np.array_equal(
bef_tQ, ancestor.tQ[allwill[sf_ancestors]])
print 'MQ stays the same', np.array_equal(
bef_MQ, ancestor.MQ[allwill[sf_ancestors]])
print 'SFR stays the same', np.array_equal(
bef_sfr, ancestor.sfr[allwill[sf_ancestors]])
print 'MshamEvol stays the same', np.array_equal(bef_MshamEvol,
MshamEvol[allwill])
print 'recalculating for just t=', t_output[0]
SFRs_evolved1, tQ1 = \
Evol_shamMstarSFR(
ancestor.tsnap_genesis[allwill[sf_ancestors]],
[t_output[0]],
tQ0=ancestor.tQ[allwill[sf_ancestors]],
MQ0=ancestor.MQ[allwill[sf_ancestors]],
SFR0=ancestor.sfr[allwill[sf_ancestors]],
q_Mshams=MshamEvol[allwill[q_ancestors],:],
sf_Mshams=MshamEvol[allwill[sf_ancestors],:],
**kwargs_sfr
)
print 'tsnap stays the same', np.array_equal(
bef_tsnap, ancestor.tsnap_genesis[allwill[sf_ancestors]])
print 'tQ stays the same', np.array_equal(
bef_tQ, ancestor.tQ[allwill[sf_ancestors]])
print 'MQ stays the same', np.array_equal(
bef_MQ, ancestor.MQ[allwill[sf_ancestors]])
print 'SFR stays the same', np.array_equal(
bef_sfr, ancestor.sfr[allwill[sf_ancestors]])
print 'MshamEvol stays the same', np.array_equal(bef_MshamEvol,
MshamEvol[allwill])
print np.array_equal(SFRs_evolved[0], SFRs_evolved1)
dist, bins = np.histogram(SFRs_evolved[0], range=[-5, 2])
plt.plot(0.5 * (bins[:-1] + bins[1:]), dist)
dist, bins = np.histogram(SFRs_evolved1, range=[-5, 2])
plt.plot(0.5 * (bins[:-1] + bins[1:]), dist)
plt.show()
for i_d in range(len(t_output)):
sf_anc = np.where(ancestor.sfr_class[wills[i_d]] == 'star-forming')[0]
sf_succession = (successions[i_d])[sf_anc]
sf_will = (wills[i_d])[sf_anc]
sf_allwill = allwill[sf_ancestors]
ss, ww = intersection_index(sf_allwill, sf_will)
if len(ww) != len(sf_will):
raise ValueError
#descendants[i_d].mass[sf_succession[ww]] = (Ms_evolved[i_d])[ss]
descendants[i_d].sfr[sf_succession[ww]] = (SFRs_evolved[i_d])[ss]
is_qing = np.where(tQ[ss] < t_output[i_d])
is_notqing = np.where(tQ[ss] >= t_output[i_d])
descendants[i_d].sfr_class[sf_succession[is_qing]] = 'quiescent'
descendants[i_d].sfr_class[sf_succession[is_notqing]] = 'star-forming'
# calculate SSFR based on evloved SFR and M*
descendants[i_d].ssfr[sf_succession[ww]] = \
descendants[i_d].sfr[sf_succession[ww]] - descendants[i_d].mass[sf_succession[ww]]
#if massevol_prop['name'] == 'integrated':
# if np.min(descendant.mass[succession[sf_ancestors]] - ancestor.mass[will[sf_ancestors]]) < 0.0:
# raise ValueError("Integrated mass can't reduce the mass")
return descendants
def _StarformingEvol_Pq(ancestor, descendant, succession=None, will=None,
sfr_prop=None, evol_prop=None, lineage=None):
''' Evolve the stellar mass and star formation rates simultaneously.
The stellar mass evolution is dictated by the SFR.
'''
sfms_prop = sfr_prop['sfms']
pq_prop = evol_prop['pq']
tau_prop = evol_prop['tau']
sfrevol_prop = evol_prop['sfr']
dutycycle_prop = sfrevol_prop['dutycycle']
massevol_prop = evol_prop['mass']
# quenching probability
pq_time = time.time()
P_q = _getPq(ancestor, descendant,
succession=succession, will=will,
pq_prop=pq_prop, sfr_prop=sfr_prop)
print 'Quenching Probabilities takes : ', time.time() - pq_time
# quenching time/redshift
tq_time = time.time()
descendant, t_q, z_q = _tQuench(ancestor, descendant,
P_q, succession=succession, will=will)
print 'tQuench takes: ', time.time() - tq_time
# star forming ancestors
sf_ancestors = np.where(ancestor.sfr_class[will] == 'star-forming')[0] # SF ancestors
# star-formation duty cycle parameters
dutycycle_time = time.time()
dutycycle_prop = sfr_evol.dutycycle_param(len(sf_ancestors), dutycycle_prop=dutycycle_prop)
dutycycle_prop['delta_sfr'] = ancestor.delta_sfr[will[sf_ancestors]]
print 'SFR dutycycle properties take ', time.time() - dutycycle_time, ' to generate'
M_q = np.repeat(-999., len(sf_ancestors))
# keywords for logSFR(M*,t)
kwargs_sfr = {
't_init': descendant.tsnap_genesis[succession[sf_ancestors]],
't_q': t_q,
'z_q': z_q,
'M_q': M_q,
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'sfms_prop': sfms_prop
}
sham_mass = descendant.mass[succession[sf_ancestors]].copy() # descendant SHAM masses
if massevol_prop['name'] == 'integrated': # integrated stellar mass
mass_time = time.time()
descendant.mass[succession[sf_ancestors]], descendant.sfr[succession[sf_ancestors]], blah = \
M_integrate(
ancestor.mass_genesis[will[sf_ancestors]],
ancestor.tsnap_genesis[will[sf_ancestors]],
descendant.t_cosmic,
massevol_prop=massevol_prop,
kwargs_sfr=kwargs_sfr # kwargs for logSFR(M*,t) function
)
if np.min(descendant.mass[succession[sf_ancestors]] - ancestor.mass[will[sf_ancestors]]) < 0.0:
raise ValueError("Integrated mass can't reduce the mass")
print 'Integrated Masses takes ', time.time() - mass_time
elif massevol_prop['name'] == 'sham': # SHAM masses
# determine the quenching stellar mass (the SHAM stellar mass at t_Q)
M_q = _SHAM_Mquenching(kwargs_sfr['t_q'], lineage=lineage,
descendant=descendant,
descendant_index=succession[sf_ancestors])
kwargs_sfr['M_q'] = M_q
descendant.sfr[succession[sf_ancestors]] = \
logSFR_M_t(sham_mass, descendant.t_cosmic, **kwargs_sfr)
# calculate SSFR based on evloved SFR and M*
descendant.ssfr[succession[sf_ancestors]] = \
descendant.sfr[succession[sf_ancestors]] - descendant.mass[succession[sf_ancestors]]
return
def _StarformingEvol_SimulEvo(ancestor, descendants, successions=None, wills=None,
sfr_prop=None, evol_prop=None, lineage=None, quiet=True):
''' Evolve the stellar mass, star formation rates, and quench galaxies simultaneously.
'''
sfms_prop = sfr_prop['sfms'] # SFMS properties
fq_prop = sfr_prop['fq'] # fq_prop
tau_prop = evol_prop['tau'] # Quenching timescale properties
fudge_prop = evol_prop['fudge']
dutycycle_prop = evol_prop['sfr']['dutycycle'] # SF dutycycle properties
massevol_prop = evol_prop['mass'] # mass evolution properties
if len(descendants) > 1:
allwill = np.array(list(set(list(np.concatenate(wills))))) # combine will of unique elements
t_output = []
for des in descendants:
t_output.append(des.t_cosmic)
else:
t_output = [descendants[0].t_cosmic]
allwill = wills[0]
sf_ancestors = np.where(ancestor.sfr_class[allwill] == 'star-forming')[0] # SF ancestors
q_ancestors = np.where(ancestor.sfr_class[allwill] == 'quiescent')[0] # Q ancestors
# star-formation duty cycle parameters
if not quiet:
dutycycle_time = time.time()
dutycycle_prop = sfr_evol.dutycycle_param(len(sf_ancestors), dutycycle_prop=dutycycle_prop)
dutycycle_prop['delta_sfr'] = ancestor.delta_sfr[allwill[sf_ancestors]]
if not quiet:
print 'SFR dutycycle properties take ', time.time() - dutycycle_time, ' to generate'
MshamEvol = ancestor.Msham_evol[0]
# keywords for logSFR(M*,t)
kwargs_sfr = {'dutycycle_prop': dutycycle_prop, 'tau_prop': tau_prop, 'fudge_prop': fudge_prop,
'fq_prop': fq_prop, 'sfms_prop': sfms_prop, 'massevol_prop': massevol_prop}
if not quiet:
mass_time = time.time()
# DEFUNCT FOR NOW. needs to be updated
#if massevol_prop['name'] == 'integrated':
# M_evolved, SFR_evolved, tQ = \
# Evol_IntegMstarSFR(
# ancestor.mass_genesis[will[sf_ancestors]],
# ancestor.tsnap_genesis[will[sf_ancestors]],
# descendant.t_cosmic,
# tQ0=ancestor.tQ[will[sf_ancestors]],
# MQ0=ancestor.MQ[will[sf_ancestors]],
# ancestor_Mq=MshamEvol[will[q_ancestors],:],
# **kwargs_sfr
# )
#elif massevol_prop['name'] == 'sham':
bef_tsnap = ancestor.tsnap_genesis[allwill[sf_ancestors]]
bef_tQ = ancestor.tQ[allwill[sf_ancestors]]
bef_MQ = ancestor.MQ[allwill[sf_ancestors]]
bef_sfr = ancestor.sfr[allwill[sf_ancestors]]
bef_MshamEvol = MshamEvol[allwill]
SFRs_evolved, tQ = \
Evol_shamMstarSFR(
ancestor.tsnap_genesis[allwill[sf_ancestors]],
t_output,
tQ0=ancestor.tQ[allwill[sf_ancestors]],
MQ0=ancestor.MQ[allwill[sf_ancestors]],
SFR0=ancestor.sfr[allwill[sf_ancestors]],
q_Mshams=MshamEvol[allwill[q_ancestors],:],
sf_Mshams=MshamEvol[allwill[sf_ancestors],:],
**kwargs_sfr
)
print 'tsnap stays the same', np.array_equal(bef_tsnap, ancestor.tsnap_genesis[allwill[sf_ancestors]])
print 'tQ stays the same', np.array_equal(bef_tQ, ancestor.tQ[allwill[sf_ancestors]])
print 'MQ stays the same', np.array_equal(bef_MQ, ancestor.MQ[allwill[sf_ancestors]])
print 'SFR stays the same', np.array_equal(bef_sfr, ancestor.sfr[allwill[sf_ancestors]])
print 'MshamEvol stays the same', np.array_equal(bef_MshamEvol, MshamEvol[allwill])
print 'recalculating for just t=', t_output[0]
SFRs_evolved1, tQ1 = \
Evol_shamMstarSFR(
ancestor.tsnap_genesis[allwill[sf_ancestors]],
[t_output[0]],
tQ0=ancestor.tQ[allwill[sf_ancestors]],
MQ0=ancestor.MQ[allwill[sf_ancestors]],
SFR0=ancestor.sfr[allwill[sf_ancestors]],
q_Mshams=MshamEvol[allwill[q_ancestors],:],
sf_Mshams=MshamEvol[allwill[sf_ancestors],:],
**kwargs_sfr
)
print 'tsnap stays the same', np.array_equal(bef_tsnap, ancestor.tsnap_genesis[allwill[sf_ancestors]])
print 'tQ stays the same', np.array_equal(bef_tQ, ancestor.tQ[allwill[sf_ancestors]])
print 'MQ stays the same', np.array_equal(bef_MQ, ancestor.MQ[allwill[sf_ancestors]])
print 'SFR stays the same', np.array_equal(bef_sfr, ancestor.sfr[allwill[sf_ancestors]])
print 'MshamEvol stays the same', np.array_equal(bef_MshamEvol, MshamEvol[allwill])
print np.array_equal(SFRs_evolved[0], SFRs_evolved1)
dist, bins = np.histogram(SFRs_evolved[0], range=[-5, 2])
plt.plot(0.5 * (bins[:-1] + bins[1:]), dist)
dist, bins = np.histogram(SFRs_evolved1, range=[-5, 2])
plt.plot(0.5 * (bins[:-1] + bins[1:]), dist)
plt.show()
for i_d in range(len(t_output)):
sf_anc = np.where(ancestor.sfr_class[wills[i_d]] == 'star-forming')[0]
sf_succession = (successions[i_d])[sf_anc]
sf_will = (wills[i_d])[sf_anc]
sf_allwill = allwill[sf_ancestors]
ss, ww = intersection_index(sf_allwill, sf_will)
if len(ww) != len(sf_will):
raise ValueError
#descendants[i_d].mass[sf_succession[ww]] = (Ms_evolved[i_d])[ss]
descendants[i_d].sfr[sf_succession[ww]] = (SFRs_evolved[i_d])[ss]
is_qing = np.where(tQ[ss] < t_output[i_d])
is_notqing = np.where(tQ[ss] >= t_output[i_d])
descendants[i_d].sfr_class[sf_succession[is_qing]] = 'quiescent'
descendants[i_d].sfr_class[sf_succession[is_notqing]] = 'star-forming'
# calculate SSFR based on evloved SFR and M*
descendants[i_d].ssfr[sf_succession[ww]] = \
descendants[i_d].sfr[sf_succession[ww]] - descendants[i_d].mass[sf_succession[ww]]
#if massevol_prop['name'] == 'integrated':
# if np.min(descendant.mass[succession[sf_ancestors]] - ancestor.mass[will[sf_ancestors]]) < 0.0:
# raise ValueError("Integrated mass can't reduce the mass")
return descendants
def _StarformingEvol(ancestor, descendant, succession=None, will=None, sfr_prop=None, evol_prop=None, lineage=None):
''' Evolve the stellar mass, star formation rates, and quench galaxies simultaneously.
'''
# SFMS properties
sfms_prop = sfr_prop['sfms']
# fq_prop
fq_prop = sfr_prop['fq']
# Quenching timescale properties
tau_prop = evol_prop['tau']
# SFR evolution properties
sfrevol_prop = evol_prop['sfr']
# SF dutycycle properties
dutycycle_prop = sfrevol_prop['dutycycle']
# mass evolution properties
massevol_prop = evol_prop['mass']
# star forming ancestors
sf_ancestors = np.where(ancestor.sfr_class[will] == 'star-forming')[0] # SF ancestors
# star-formation duty cycle parameters
dutycycle_time = time.time()
dutycycle_prop = sfr_evol.dutycycle_param(len(sf_ancestors), dutycycle_prop=dutycycle_prop)
dutycycle_prop['delta_sfr'] = ancestor.delta_sfr[will[sf_ancestors]]
print 'SFR dutycycle properties take ', time.time() - dutycycle_time, ' to generate'
# keywords for logSFR(M*,t)
kwargs_sfr = {
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'fq_prop': fq_prop,
'sfms_prop': sfms_prop,
'massevol_prop': massevol_prop
}
sham_mass = descendant.mass[succession[sf_ancestors]].copy() # descendant SHAM masses
mass_time = time.time()
descendant.mass[succession[sf_ancestors]], descendant.sfr[succession[sf_ancestors]] = \
MstarSFR_simul_evol(
ancestor.mass_genesis[will[sf_ancestors]],
ancestor.tsnap_genesis[will[sf_ancestors]],
descendant.t_cosmic,
**kwargs_sfr
)
if np.min(descendant.mass[succession[sf_ancestors]] - ancestor.mass[will[sf_ancestors]]) < 0.0:
raise ValueError("Integrated mass can't reduce the mass")
'''
elif massevol_prop['name'] == 'sham': # SHAM masses
# determine the quenching stellar mass (the SHAM stellar mass at t_Q)
M_q = _SHAM_Mquenching(kwargs_sfr['t_q'], lineage=lineage,
descendant=descendant,
descendant_index=succession[sf_ancestors])
kwargs_sfr['M_q'] = M_q
descendant.sfr[succession[sf_ancestors]] = \
logSFR_M_t(sham_mass, descendant.t_cosmic, **kwargs_sfr)
'''
# calculate SSFR based on evloved SFR and M*
descendant.ssfr[succession[sf_ancestors]] = \
descendant.sfr[succession[sf_ancestors]] - descendant.mass[succession[sf_ancestors]]
return
def _StarformingEvol(ancestor,
descendant,
succession=None,
will=None,
sfr_prop=None,
evol_prop=None,
lineage=None):
''' Evolve the stellar mass, star formation rates, and quench galaxies simultaneously.
'''
# SFMS properties
sfms_prop = sfr_prop['sfms']
# fq_prop
fq_prop = sfr_prop['fq']
# Quenching timescale properties
tau_prop = evol_prop['tau']
# SFR evolution properties
sfrevol_prop = evol_prop['sfr']
# SF dutycycle properties
dutycycle_prop = sfrevol_prop['dutycycle']
# mass evolution properties
massevol_prop = evol_prop['mass']
# star forming ancestors
sf_ancestors = np.where(
ancestor.sfr_class[will] == 'star-forming')[0] # SF ancestors
# star-formation duty cycle parameters
dutycycle_time = time.time()
dutycycle_prop = sfr_evol.dutycycle_param(len(sf_ancestors),
dutycycle_prop=dutycycle_prop)
dutycycle_prop['delta_sfr'] = ancestor.delta_sfr[will[sf_ancestors]]
print 'SFR dutycycle properties take ', time.time(
) - dutycycle_time, ' to generate'
# keywords for logSFR(M*,t)
kwargs_sfr = {
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'fq_prop': fq_prop,
'sfms_prop': sfms_prop,
'massevol_prop': massevol_prop
}
sham_mass = descendant.mass[
succession[sf_ancestors]].copy() # descendant SHAM masses
mass_time = time.time()
descendant.mass[succession[sf_ancestors]], descendant.sfr[succession[sf_ancestors]] = \
MstarSFR_simul_evol(
ancestor.mass_genesis[will[sf_ancestors]],
ancestor.tsnap_genesis[will[sf_ancestors]],
descendant.t_cosmic,
**kwargs_sfr
)
if np.min(descendant.mass[succession[sf_ancestors]] -
ancestor.mass[will[sf_ancestors]]) < 0.0:
raise ValueError("Integrated mass can't reduce the mass")
'''
elif massevol_prop['name'] == 'sham': # SHAM masses
# determine the quenching stellar mass (the SHAM stellar mass at t_Q)
M_q = _SHAM_Mquenching(kwargs_sfr['t_q'], lineage=lineage,
descendant=descendant,
descendant_index=succession[sf_ancestors])
kwargs_sfr['M_q'] = M_q
descendant.sfr[succession[sf_ancestors]] = \
logSFR_M_t(sham_mass, descendant.t_cosmic, **kwargs_sfr)
'''
# calculate SSFR based on evloved SFR and M*
descendant.ssfr[succession[sf_ancestors]] = \
descendant.sfr[succession[sf_ancestors]] - descendant.mass[succession[sf_ancestors]]
return