def Plot_dFqdt():
''' Plot the evolution of the derivative of the quiescent fraction as a function of M* and t
for different parameterized models of f_Q
d f_Q / d t
'''
prettyplot()
pretty_colors = prettycolors()
dt = 0.01
M_arr = np.arange(9.0, 12.5, 0.5)
t_arr = np.arange(6.0, 13.0, 0.2)
fig = plt.figure()
sub = fig.add_subplot(111)
for iM, Mstar in enumerate(M_arr):
qf = gp.Fq()
lit = 'wetzelsmooth'
sub.plot(t_arr, [gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_arr],
ls='-',
lw=3,
c=pretty_colors[iM],
label=r"$\mathtt{M}_* = " + str(round(Mstar, 2)) + "$")
lit = 'wetzel'
sub.plot(t_arr, [gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_arr],
ls=':',
lw=3,
c=pretty_colors[iM])
lit = 'cosmosinterp'
t_blah = np.arange(8.0, 9.6, 0.1)
sub.plot(t_blah,
[gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_blah],
ls='--',
lw=3,
c=pretty_colors[iM])
lit = 'cosmosfit'
t_blah = np.arange(8.0, 9.6, 0.1)
sub.plot(t_blah,
[gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_blah],
ls='-.',
lw=3,
c=pretty_colors[iM])
sub.set_xlim([t_arr.min(), t_arr.max()])
sub.legend(loc='upper left')
sub.set_ylim([0., 0.2])
#plt.show()
fig_file = ''.join(['figure/test/', 'dfqdt.png'])
fig.savefig(fig_file, bbox_inches='tight')
return None
def Evol_shamMstarSFR(t0,
tfs,
t_step=0.5,
tQ0=None,
MQ0=None,
SFR0=None,
q_Mshams=None,
sf_Mshams=None,
quiet=True,
**kwargs):
''' Evolve SFR of the SF galaxies while quenching some of them at the same time.
Notes
-----
* SF galaxies are quenched based on the following prescription:
The number of galaxies that start quenching between t0 and t0+tstep
is determined by first guessing,
N_quenching = N_sf(t0) * (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
'''
dutycycle_prop = kwargs['dutycycle_prop']
tau_prop = kwargs['tau_prop']
fq_prop = kwargs['fq_prop']
sfms_prop = kwargs['sfms_prop']
massevol_prop = kwargs['massevol_prop']
fudge_prop = kwargs['fudge_prop']
tQ = tQ0.copy()
Mq = MQ0.copy()
t00 = t0.min() # earliest starting time (cosmic time of ancestor snapshot)
t_evol = t_snap[np.abs(t_snap - np.max(tfs)).argmin() -
1:np.abs(t_snap - t00).argmin() + 1][::-1]
qf = Fq()
Fq_anal = qf.model
# Mass bins
M_bins = np.arange(6.0, 13., 0.5)
M_mid = 0.5 * (M_bins[:-1] + M_bins[1:])
for i_t, tt in enumerate(t_evol[:-1]):
if not quiet:
print 't_cosmic = ', tt
t_one_tstep = time.time()
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t0_snap = np.abs(t_snap - tt).argmin() - 1
Msham_q0 = q_Mshams[:, closest_t0_snap].copy()
Msham_sf0 = sf_Mshams[:, closest_t0_snap].copy()
within = np.where((Msham_sf0 > 0.) & (t0 <= tt)) # tsnap_genesis <= t
sf_within = np.where((Msham_sf0 > 0.) & (t0 <= tt)
& (tQ == 999.))[0] # Not quenching SF galaxies
Nsf_0 = len(sf_within)
M_t0_sample = np.concatenate(
[Msham_sf0[within], Msham_q0[np.where(Msham_q0 > 0.)]])
P_sf = np.random.uniform(0., 1., Nsf_0)
# Initial estimate of quenching probability given by dFq/dt
# P_q = (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
Ng0, dum = np.histogram(M_t0_sample, bins=M_bins)
Nsf0, dum = np.histogram(Msham_sf0[sf_within], bins=M_bins)
P_q_arr = Ng0 / Nsf0 * dFqdt(
M_mid, tt + 0.5 * t_step, lit=fq_prop['name']) * t_step
P_q_arr[np.where(Nsf0 == 0)] = 0.
if not quiet:
print 'M* : ', M_mid[-6:]
print 'P_q : ', P_q_arr[-6:]
Pq_M = interpolate.interp1d(M_mid, P_q_arr, kind='linear')
P_q = Pq_M(Msham_sf0[sf_within])
q_ing0 = np.where(P_q > P_sf)
Nqing0 = len(q_ing0[0])
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t_snap = np.abs(t_snap - t_evol[i_t + 1]).argmin() - 1
Msham_q1 = q_Mshams[:, closest_t_snap]
Msham_sf1 = sf_Mshams[:, closest_t_snap]
M_t1_sample = np.concatenate([Msham_sf1[within], Msham_q1])
# dPQ correction to the quenching to account for change in Ng(M*,t)
Ngp, dum = np.histogram(M_t1_sample, bins=M_bins)
dPq = (Ngp - Ng0) / Nsf0.astype('float') * Fq_anal(
M_mid, z_of_t(tt + t_step), lit=fq_prop['name'])
dPq[np.where(dPq < 0.)] = 0.
dPq[np.where(Nsf0 == 0)] = 0.
if not quiet:
print 'dPq : ', dPq[-6:]
dPq_M = interpolate.interp1d(M_mid, dPq, kind='linear')
P_q += dPq_M(Msham_sf0[sf_within])
# fudge factor for P_Q
fudge_factor = fudge_prop['slope'] * (
Msham_sf0[sf_within] -
fudge_prop['fidmass']) + fudge_prop['offset']
fudge_factor[np.where(fudge_factor < 1.)] = 1.
P_q *= fudge_factor
q_ing = np.where(P_sf < P_q)
if not quiet:
print 'Initial guess ', Nqing0, ' final: ', len(
q_ing[0]), ' SF gal out of ', Nsf_0, ' start quenching'
print time.time() - t_one_tstep
print '----------------'
# assign them quenching times then actually evolve their stellar masses
tQ[sf_within[q_ing]] = np.random.uniform(low=tt,
high=tt + t_step,
size=len(q_ing[0]))
quenching = np.where((Mq == -999.) & (tQ < 999.) & (tQ > 0))[0]
Nquenching = len(quenching)
closest_tQ_index = np.abs(
np.tile(t_snap, (Nquenching, 1)) -
np.tile(tQ[quenching].reshape(Nquenching, 1),
(1, len(t_snap)))).argmin(axis=1) - 1
Mq[quenching] = sf_Mshams[quenching, closest_tQ_index].copy()
SFR_list = []
for tf in tfs:
closest_tf_snap = np.abs(t_snap - tf).argmin() - 1
Msham_f = sf_Mshams[:, closest_tf_snap].copy()
if closest_tf_snap == 0:
before = Msham_f[:10]
kwargs_sfr = {
't_init': t0.copy(),
't_q': tQ.copy(),
'M_q': Mq.copy(),
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'sfms_prop': sfms_prop
}
SFR, M_qq = logSFR_M_t(Msham_f.copy(), tf, **kwargs_sfr)
if closest_tf_snap == 0:
print 'is same? ', np.array_equal(before, Msham_f[:10])
print tf, SFR.min(), SFR.max()
# correct for the galaxies that were originally in the green valley.
# ultimately doesn't make a difference in the outcome, but to be meticulous
gv0 = np.where(tQ0 == 0.) # galaxies that were initiall quenching
SFR[gv0] -= sfr_evol.AverageLogSFR_sfms(Mq[gv0],
z_of_t(t0[gv0]),
sfms_prop=sfms_prop)
SFR[gv0] -= sfr_evol.DeltaLogSFR_quenching(tQ[gv0],
t0[gv0],
M_q=Mq[gv0],
tau_prop=tau_prop)
SFR[gv0] += SFR0[gv0].copy()
SFR_list.append(SFR.copy())
del SFR
return SFR_list, tQ.copy()
def MstarSFR_simul_evol_OBSOLETE(M0,
t0,
tf,
t_step=0.2,
ancestor_Mq=None,
quiet=True,
**kwargs):
''' Evolve stellar mass, SFR, and quench galaxies simultaneously.
Notes
-----
* SF galaxies are quenched based on the following prescription:
The number of galaxies that start quenching between t0 and t0+tstep
is determined by,
N_quenching = N_sf(t0) * (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
'''
dutycycle_prop = kwargs['dutycycle_prop']
tau_prop = kwargs['tau_prop']
fq_prop = kwargs['fq_prop']
sfms_prop = kwargs['sfms_prop']
massevol_prop = kwargs['massevol_prop']
Mstar = M0
SFR = np.repeat(-999., len(M0))
tQ = np.repeat(999., len(M0))
Mq = np.repeat(-999., len(M0))
t00 = t0.min() # earliest starting time (cosmic time of ancestor snapshot)
t_evol = np.arange(t00, tf + t_step, t_step)
t_evol[-1] = tf
qf = Fq()
Fq_anal = qf.model
for tt in t_evol:
if not quiet:
print 't_cosmic = ', tt
within = np.where(t0 <= tt) # tsnap_genesis <= t
sf_within = np.where((t0 <= tt)
& (tQ == 999.))[0] # Not quenching SF galaxies
Nsf_0 = len(sf_within)
P_sf = np.random.uniform(0., 1., Nsf_0)
# Initial estimate of P_q = (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
t_offset = np.zeros(len(sf_within))
#t_offset = -2.7 * (Mstar[sf_within] - 11.5)
#t_offset[np.where(t_offset < 0.)] = 0.
fudge = 1. # fudge factor
f_Ng = (
1. /
(1. - Fq_anal(Mstar[sf_within], z_of_t(tt), lit=fq_prop['name'])))
whereinf = np.where(f_Ng == np.inf)
P_q = fudge * f_Ng * dFqdt(Mstar[sf_within],
tt + t_offset + 0.5 * t_step,
lit=fq_prop['name']) * t_step
P_q[whereinf] = 1.0
q_ing = np.where(P_q > P_sf)
if not quiet:
print 'P_q', P_q.min(), P_q.max(), P_q.mean()
print 'Initial guess ', len(
q_ing[0]
), ' SF galaxies out of ', Nsf_0, ' galaxies start quenching'
# assign them quenching times
tQ[sf_within[q_ing]] = np.random.uniform(low=tt,
high=tt + t_step,
size=len(q_ing[0]))
kwargs_sfr = {
't_init': t0[within],
't_q': tQ[within],
'M_q': Mq[within],
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'sfms_prop': sfms_prop,
'indices': within
}
M_evol, sfr_evol, Mq_evol = M_integrate(Mstar[within],
tt,
tt + t_step,
massevol_prop=massevol_prop,
kwargs_sfr=kwargs_sfr)
Mstar[within] = M_evol
SFR[within] = sfr_evol
Mq[within] = Mq_evol
if SFR.min() == -999.:
raise ValueError
return Mstar, SFR, tQ
def _Evol_MshamSFR(self, nsnap_d, allwill, sf_ancestor, q_ancestor):
''' Evolve SFR of the SF galaxies while quenching some of them at the same time.
Notes
-----
* SF galaxies are quenched based on the following prescription:
The number of galaxies that start quenching between t0 and t0+tstep
is determined by first guessing,
N_quenching = N_sf(t0) * (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
'''
tQ0 = self.ancestor.tQ[allwill[sf_ancestor]].copy()
tQ = self.ancestor.tQ[allwill[sf_ancestor]].copy()
Mq = self.ancestor.MQ[allwill[sf_ancestor]].copy()
# earliest starting time (cosmic time of ancestor snapshot)
t0 = self.ancestor.tsnap_genesis[allwill[sf_ancestor]].copy()
t00 = t0.min()
i_low = np.abs(t_snap-np.max(self.tsnap_descendants)).argmin()-1
i_high = np.abs(t_snap-t00).argmin()+1
t_evol = t_snap[i_low:i_high][::-1]
qf = Fq()
Fq_anal = qf.model
M_bins = np.arange(5.5, 13., 0.5)
M_mid = 0.5 * (M_bins[:-1] + M_bins[1:])
q_Mshams = self.MshamEvol[allwill[q_ancestor],:].copy()
sf_Mshams = self.MshamEvol[allwill[sf_ancestor],:].copy()
for i_t, tt in enumerate(t_evol[:-1]):
t_step = t_evol[i_t+1] - tt
if not self.quiet:
print 't_cosmic = ', tt
t_one_tstep = time.time()
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t0_snap = np.abs(t_snap-tt).argmin() - 1
Msham_q0 = q_Mshams[:, closest_t0_snap]
Msham_sf0 = sf_Mshams[:, closest_t0_snap]
within = np.where((Msham_sf0 > 0.) & (t0 <= tt)) # tsnap_genesis <= t
sf_within = np.where((Msham_sf0 > 0.) & (t0 <= tt) & (tQ == 999.))[0] # Not quenching SF galaxies
Nsf_0 = len(sf_within)
M_t0_sample = np.concatenate(
[Msham_sf0[within], Msham_q0[np.where(Msham_q0 > 0.)]]
)
P_sf = np.random.uniform(0., 1., Nsf_0)
# Initial estimate of quenching probability given by dFq/dt
# P_q = (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
Ng0, dum = np.histogram(M_t0_sample, bins=M_bins)
Nsf0, dum = np.histogram(Msham_sf0[sf_within], bins=M_bins)
P_q_arr = Ng0/Nsf0 * \
dFqdt(M_mid, tt + 0.5 * t_step, lit=self.fq_prop['name']) * t_step
P_q_arr[np.where(Nsf0 == 0)] = 0.
if not self.quiet:
print 'M* : ', M_mid[-6:]
print 'P_q : ', P_q_arr[-6:]
Pq_M = interpolate.interp1d(M_mid, P_q_arr, kind='linear')
P_q = Pq_M(Msham_sf0[sf_within])
q_ing0 = np.where(P_q > P_sf)
Nqing0 = len(q_ing0[0])
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t1_snap = np.abs(t_snap - t_evol[i_t+1]).argmin() - 1
Msham_q1 = q_Mshams[:, closest_t1_snap]
Msham_sf1 = sf_Mshams[:, closest_t1_snap]
M_t1_sample = np.concatenate([Msham_sf1[within], Msham_q1])
# dPQ correction to the quenching to account for change in Ng(M*,t)
Ngp, dum = np.histogram(M_t1_sample, bins=M_bins)
dPq = (Ngp - Ng0)/Nsf0.astype('float') * \
Fq_anal(M_mid, z_of_t(tt + t_step), lit=self.fq_prop['name'])
dPq[np.where(dPq < 0.)] = 0.
dPq[np.where(Nsf0 == 0)] = 0.
if not self.quiet:
print 'dPq : ', dPq[-6:]
dPq_M = interpolate.interp1d(M_mid, dPq, kind='linear')
P_q += dPq_M(Msham_sf0[sf_within])
# fudge factor for P_Q
fudge_factor = self.fudge_prop['slope'] * (Msham_sf0[sf_within] - self.fudge_prop['fidmass']) + self.fudge_prop['offset']
fudge_factor[np.where(fudge_factor < 1.)] = 1.
P_q *= fudge_factor
if self.printPQ: # store quenching probabilities
Pq_out = {}
Pq_out['mass'] = M_mid
fPQ = self.fudge_prop['slope'] * (M_mid - self.fudge_prop['fidmass']) + \
self.fudge_prop['offset']
fPQ[np.where(fPQ < 1.)] = 1.
Pq_out['Pq'] = fPQ * (P_q_arr + dPq)
Pq_out['Pq_fid'] = P_q_arr + dPq
if self.PQs is None:
self.PQs = {}
self.PQs[str(z_of_t(tt))] = Pq_out
q_ing = np.where(P_sf < P_q)
if not self.quiet:
print 'Initial guess ', Nqing0, ' final: ', len(q_ing[0]), ' SF gal out of ', Nsf_0, ' start quenching'
print time.time() - t_one_tstep
print '----------------'
# assign them quenching times then actually evolve their stellar masses
tQ[sf_within[q_ing]] = np.random.uniform(low=tt, high=tt+t_step, size=len(q_ing[0]))
quenching = np.where((Mq == -999.) & (tQ < 999.) & (tQ > 0.))[0]
Nquenching = len(quenching)
closest_tQ_index = np.abs(
np.tile(t_snap, (Nquenching, 1)) - np.tile(tQ[quenching].reshape(Nquenching, 1), (1, len(t_snap)))
).argmin(axis=1) - 1
Mq[quenching] = sf_Mshams[quenching, closest_tQ_index]
z0 = z_of_t(t0) # initial redshift
gv0 = np.where(tQ0 == 0.)[0] # galaxies that were initial quenching
t_f = self.descendant_dict[str(nsnap_d)].t_cosmic
z_f = self.descendant_dict[str(nsnap_d)].zsnap
closest_tf_snap = np.abs(t_snap - t_f).argmin() - 1
Msham_f = sf_Mshams[:, closest_tf_snap].copy()
q_ed = np.where(t_f > tQ)
tmp_M = Msham_f.copy()
tmp_M[q_ed] = Mq[q_ed].copy()
# log(SFR)_SFMS evolution from t0
logsfr_sfms = sfr_evol.AverageLogSFR_sfms(tmp_M, z0, sfms_prop=self.sfms_prop) + \
sfr_evol.DeltaLogSFR_sfms(z0, z_f, sfms_prop=self.sfms_prop)
# log(SFR)_duty cycle evolution from t0 to tQ
logsfr_sfduty = sfr_evol.DeltaLogSFR_dutycycle(
t0,
t_f,
t_q=tQ,
dutycycle_prop=self.dutycycle_prop
)
logsfr_quench = sfr_evol.DeltaLogSFR_quenching(
tQ,
t_f,
M_q=Mq,
tau_prop=self.tau_prop)
logSFR = logsfr_sfms + logsfr_quench + logsfr_sfduty
# correct for the galaxies that were originally in the green valley.
# ultimately doesn't make a difference in the outcome, but to be meticulous
logSFR[gv0] = (self.ancestor.sfr[allwill[sf_ancestor]])[gv0] + \
sfr_evol.DeltaLogSFR_sfms(z0[gv0], z_f, sfms_prop=self.sfms_prop) + \
sfr_evol.DeltaLogSFR_quenching(
np.repeat(self.ancestor.t_cosmic, len(gv0)),
t_f, M_q=Mq[gv0], tau_prop=self.tau_prop)
return logSFR, tQ
def Evol_IntegMstarSFR(M0,
t0,
tf,
t_step=0.5,
tQ0=None,
MQ0=None,
ancestor_Mq=None,
**kwargs):
''' Evolve stellar mass and SFR of the SF galaxies while quenching some of them
at the same time.
Notes
-----
* SF galaxies are quenched based on the following prescription:
The number of galaxies that start quenching between t0 and t0+tstep
is determined by first guessing,
N_quenching = N_sf(t0) * (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
'''
dutycycle_prop = kwargs['dutycycle_prop']
tau_prop = kwargs['tau_prop']
fq_prop = kwargs['fq_prop']
sfms_prop = kwargs['sfms_prop']
massevol_prop = kwargs['massevol_prop']
fudge_prop = kwargs['fudge_prop']
Mstar = M0
SFR = np.repeat(-999., len(M0))
tQ = tQ0
Mq = MQ0
t00 = t0.min() # earliest starting time (cosmic time of ancestor snapshot)
t_evol = np.arange(t00, tf + t_step, t_step)
t_evol[-1] = tf
qf = Fq()
Fq_anal = qf.model
# Mass bins
M_bins = np.arange(6.0, 13., 0.5)
M_mid = 0.5 * (M_bins[:-1] + M_bins[1:])
for tt in t_evol:
print 't_cosmic = ', tt
t_one_tstep = time.time()
within = np.where(t0 <= tt) # tsnap_genesis <= t
sf_within = np.where((t0 <= tt)
& (tQ == 999.))[0] # Not quenching SF galaxies
Nsf_0 = len(sf_within)
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t0_snap = np.abs(t_snap - tt).argmin() - 1
Msham_q0 = ancestor_Mq[:, closest_t0_snap]
M_t0_sample = np.concatenate(
[Mstar[within], Msham_q0[np.where(Msham_q0 > 0.)]])
P_sf = np.random.uniform(0., 1., Nsf_0)
# Initial estimate of quenching probability given by dFq/dt
# P_q = (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
Ng0, dum = np.histogram(M_t0_sample, bins=M_bins)
Nsf0, dum = np.histogram(Mstar[sf_within], bins=M_bins)
P_q_arr = Ng0 / Nsf0 * dFqdt(
M_mid, tt + 0.5 * t_step, lit=fq_prop['name']) * t_step
P_q_arr[np.where(Nsf0 == 0)] = 0.
print 'M* : ', M_mid[-6:]
print 'P_q : ', P_q_arr[-6:]
Pq_M = interpolate.interp1d(M_mid, P_q_arr, kind='linear')
P_q = Pq_M(Mstar[sf_within])
q_ing = np.where(P_q > P_sf)
Nqing0 = len(q_ing[0])
# assign them quenching times
tQ_tmp = tQ
tQ_tmp[sf_within[q_ing]] = np.random.uniform(low=tt,
high=tt + t_step,
size=Nqing0)
kwargs_sfr = {
't_init': t0[within],
't_q': tQ_tmp[within],
'M_q': Mq[within],
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'sfms_prop': sfms_prop,
'indices': within
}
M_evol, sfr_evol, Mq_evol = M_integrate(Mstar[within],
tt,
tt + t_step,
massevol_prop=massevol_prop,
kwargs_sfr=kwargs_sfr)
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t_snap = np.abs(t_snap - tt - t_step).argmin() - 1
Msham_qf = ancestor_Mq[:, closest_t_snap]
M_tf_sample = np.concatenate([M_evol, Msham_qf])
# dPQ correction to the quenching to account for change in Ng(M*,t)
Ngp, dum = np.histogram(M_tf_sample, bins=M_bins)
dPq = (Ngp - Ng0) / Nsf0.astype('float') * Fq_anal(
M_mid, z_of_t(tt + t_step), lit=fq_prop['name'])
dPq[np.where(dPq < 0.)] = 0.
dPq[np.where(Nsf0 == 0)] = 0.
print 'dPq : ', dPq[-6:]
dPq_M = interpolate.interp1d(M_mid, dPq, kind='linear')
P_q += dPq_M(Mstar[sf_within])
fudge_factor = fudge_prop['slope'] * (
Mstar[sf_within] - fudge_prop['fidmass']) + fudge_prop['offset']
fudge_factor[np.where(fudge_factor < 1.)] = 1.
P_q *= fudge_factor
q_ing = np.where(P_sf < P_q)
print 'Initial guess ', Nqing0, ' after correction: ', len(
q_ing[0]
), ' SF galaxies out of ', Nsf_0, ' galaxies start quenching'
print time.time() - t_one_tstep
print '----------------'
# assign them quenching times then actually evolve their stellar masses
tQ[sf_within[q_ing]] = np.random.uniform(low=tt,
high=tt + t_step,
size=len(q_ing[0]))
kwargs_sfr = {
't_init': t0[within],
't_q': tQ[within],
'M_q': Mq[within],
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'sfms_prop': sfms_prop,
'indices': within
}
M_evol, sfr_evol, Mq_evol = M_integrate(Mstar[within],
tt,
tt + t_step,
massevol_prop=massevol_prop,
kwargs_sfr=kwargs_sfr)
Mstar[within] = M_evol
SFR[within] = sfr_evol
Mq[within] = Mq_evol
if SFR.min() == -999.:
raise ValueError
return Mstar, SFR, tQ
def MstarSFR_simul_evol_OBSOLETE(M0, t0, tf, t_step=0.2, ancestor_Mq=None, quiet=True, **kwargs):
''' Evolve stellar mass, SFR, and quench galaxies simultaneously.
Notes
-----
* SF galaxies are quenched based on the following prescription:
The number of galaxies that start quenching between t0 and t0+tstep
is determined by,
N_quenching = N_sf(t0) * (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
'''
dutycycle_prop = kwargs['dutycycle_prop']
tau_prop = kwargs['tau_prop']
fq_prop = kwargs['fq_prop']
sfms_prop = kwargs['sfms_prop']
massevol_prop = kwargs['massevol_prop']
Mstar = M0
SFR = np.repeat(-999., len(M0))
tQ = np.repeat(999., len(M0))
Mq = np.repeat(-999., len(M0))
t00 = t0.min() # earliest starting time (cosmic time of ancestor snapshot)
t_evol = np.arange(t00, tf+t_step, t_step)
t_evol[-1] = tf
qf = Fq()
Fq_anal = qf.model
for tt in t_evol:
if not quiet:
print 't_cosmic = ', tt
within = np.where(t0 <= tt) # tsnap_genesis <= t
sf_within = np.where((t0 <= tt) & (tQ == 999.))[0] # Not quenching SF galaxies
Nsf_0 = len(sf_within)
P_sf = np.random.uniform(0., 1., Nsf_0)
# Initial estimate of P_q = (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
t_offset = np.zeros(len(sf_within))
#t_offset = -2.7 * (Mstar[sf_within] - 11.5)
#t_offset[np.where(t_offset < 0.)] = 0.
fudge = 1. # fudge factor
f_Ng = (1./(1.- Fq_anal(Mstar[sf_within], z_of_t(tt), lit=fq_prop['name'])))
whereinf = np.where(f_Ng == np.inf)
P_q = fudge * f_Ng * dFqdt(Mstar[sf_within], tt + t_offset + 0.5 * t_step, lit=fq_prop['name']) * t_step
P_q[whereinf] = 1.0
q_ing = np.where(P_q > P_sf)
if not quiet:
print 'P_q', P_q.min(), P_q.max(), P_q.mean()
print 'Initial guess ', len(q_ing[0]), ' SF galaxies out of ', Nsf_0, ' galaxies start quenching'
# assign them quenching times
tQ[sf_within[q_ing]] = np.random.uniform(low=tt, high=tt+t_step, size=len(q_ing[0]))
kwargs_sfr = {
't_init': t0[within],
't_q': tQ[within],
'M_q': Mq[within],
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'sfms_prop': sfms_prop,
'indices': within
}
M_evol, sfr_evol, Mq_evol = M_integrate(Mstar[within], tt, tt+t_step, massevol_prop=massevol_prop, kwargs_sfr=kwargs_sfr)
Mstar[within] = M_evol
SFR[within] = sfr_evol
Mq[within] = Mq_evol
if SFR.min() == -999.:
raise ValueError
return Mstar, SFR, tQ
def Evol_shamMstarSFR(t0, tfs, t_step=0.5, tQ0=None, MQ0=None, SFR0=None, q_Mshams=None,
sf_Mshams=None, quiet=True, **kwargs):
''' Evolve SFR of the SF galaxies while quenching some of them at the same time.
Notes
-----
* SF galaxies are quenched based on the following prescription:
The number of galaxies that start quenching between t0 and t0+tstep
is determined by first guessing,
N_quenching = N_sf(t0) * (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
'''
dutycycle_prop = kwargs['dutycycle_prop']
tau_prop = kwargs['tau_prop']
fq_prop = kwargs['fq_prop']
sfms_prop = kwargs['sfms_prop']
massevol_prop = kwargs['massevol_prop']
fudge_prop = kwargs['fudge_prop']
tQ = tQ0.copy()
Mq = MQ0.copy()
t00 = t0.min() # earliest starting time (cosmic time of ancestor snapshot)
t_evol = t_snap[np.abs(t_snap-np.max(tfs)).argmin()-1: np.abs(t_snap-t00).argmin()+1][::-1]
qf = Fq()
Fq_anal = qf.model
# Mass bins
M_bins = np.arange(6.0, 13., 0.5)
M_mid = 0.5 * (M_bins[:-1] + M_bins[1:])
for i_t, tt in enumerate(t_evol[:-1]):
if not quiet:
print 't_cosmic = ', tt
t_one_tstep = time.time()
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t0_snap = np.abs(t_snap-tt).argmin() - 1
Msham_q0 = q_Mshams[:, closest_t0_snap].copy()
Msham_sf0 = sf_Mshams[:, closest_t0_snap].copy()
within = np.where((Msham_sf0 > 0.) & (t0 <= tt)) # tsnap_genesis <= t
sf_within = np.where((Msham_sf0 > 0.) & (t0 <= tt) & (tQ == 999.))[0] # Not quenching SF galaxies
Nsf_0 = len(sf_within)
M_t0_sample = np.concatenate([Msham_sf0[within], Msham_q0[np.where(Msham_q0 > 0.)]])
P_sf = np.random.uniform(0., 1., Nsf_0)
# Initial estimate of quenching probability given by dFq/dt
# P_q = (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
Ng0, dum = np.histogram(M_t0_sample, bins=M_bins)
Nsf0, dum = np.histogram(Msham_sf0[sf_within], bins=M_bins)
P_q_arr = Ng0/Nsf0 * dFqdt(M_mid, tt + 0.5 * t_step, lit=fq_prop['name']) * t_step
P_q_arr[np.where(Nsf0 == 0)] = 0.
if not quiet:
print 'M* : ', M_mid[-6:]
print 'P_q : ', P_q_arr[-6:]
Pq_M = interpolate.interp1d(M_mid, P_q_arr, kind='linear')
P_q = Pq_M(Msham_sf0[sf_within])
q_ing0 = np.where(P_q > P_sf)
Nqing0 = len(q_ing0[0])
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t_snap = np.abs(t_snap - t_evol[i_t+1]).argmin() - 1
Msham_q1 = q_Mshams[:, closest_t_snap]
Msham_sf1 = sf_Mshams[:, closest_t_snap]
M_t1_sample = np.concatenate([Msham_sf1[within], Msham_q1])
# dPQ correction to the quenching to account for change in Ng(M*,t)
Ngp, dum = np.histogram(M_t1_sample, bins=M_bins)
dPq = (Ngp - Ng0)/Nsf0.astype('float') * Fq_anal(M_mid, z_of_t(tt + t_step), lit=fq_prop['name'])
dPq[np.where(dPq < 0.)] = 0.
dPq[np.where(Nsf0 == 0)] = 0.
if not quiet:
print 'dPq : ', dPq[-6:]
dPq_M = interpolate.interp1d(M_mid, dPq, kind='linear')
P_q += dPq_M(Msham_sf0[sf_within])
# fudge factor for P_Q
fudge_factor = fudge_prop['slope'] * (Msham_sf0[sf_within] - fudge_prop['fidmass']) + fudge_prop['offset']
fudge_factor[np.where(fudge_factor < 1.)] = 1.
P_q *= fudge_factor
q_ing = np.where(P_sf < P_q)
if not quiet:
print 'Initial guess ', Nqing0, ' final: ', len(q_ing[0]), ' SF gal out of ', Nsf_0, ' start quenching'
print time.time() - t_one_tstep
print '----------------'
# assign them quenching times then actually evolve their stellar masses
tQ[sf_within[q_ing]] = np.random.uniform(low=tt, high=tt+t_step, size=len(q_ing[0]))
quenching = np.where((Mq == -999.) & (tQ < 999.) & (tQ > 0))[0]
Nquenching = len(quenching)
closest_tQ_index = np.abs(
np.tile(t_snap, (Nquenching,1)) - np.tile(tQ[quenching].reshape(Nquenching,1), (1, len(t_snap)))
).argmin(axis=1) - 1
Mq[quenching] = sf_Mshams[quenching, closest_tQ_index].copy()
SFR_list = []
for tf in tfs:
closest_tf_snap = np.abs(t_snap-tf).argmin() - 1
Msham_f = sf_Mshams[:, closest_tf_snap].copy()
if closest_tf_snap == 0:
before = Msham_f[:10]
kwargs_sfr = {
't_init': t0.copy(),
't_q': tQ.copy(),
'M_q': Mq.copy(),
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'sfms_prop': sfms_prop
}
SFR, M_qq = logSFR_M_t(Msham_f.copy(), tf, **kwargs_sfr)
if closest_tf_snap == 0:
print 'is same? ', np.array_equal(before, Msham_f[:10])
print tf, SFR.min(), SFR.max()
# correct for the galaxies that were originally in the green valley.
# ultimately doesn't make a difference in the outcome, but to be meticulous
gv0 = np.where(tQ0 == 0.) # galaxies that were initiall quenching
SFR[gv0] -= sfr_evol.AverageLogSFR_sfms(Mq[gv0], z_of_t(t0[gv0]), sfms_prop=sfms_prop)
SFR[gv0] -= sfr_evol.DeltaLogSFR_quenching(tQ[gv0], t0[gv0], M_q=Mq[gv0], tau_prop=tau_prop)
SFR[gv0] += SFR0[gv0].copy()
SFR_list.append(SFR.copy())
del SFR
return SFR_list, tQ.copy()
def Evol_IntegMstarSFR(M0, t0, tf, t_step=0.5, tQ0=None, MQ0=None, ancestor_Mq=None, **kwargs):
''' Evolve stellar mass and SFR of the SF galaxies while quenching some of them
at the same time.
Notes
-----
* SF galaxies are quenched based on the following prescription:
The number of galaxies that start quenching between t0 and t0+tstep
is determined by first guessing,
N_quenching = N_sf(t0) * (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
'''
dutycycle_prop = kwargs['dutycycle_prop']
tau_prop = kwargs['tau_prop']
fq_prop = kwargs['fq_prop']
sfms_prop = kwargs['sfms_prop']
massevol_prop = kwargs['massevol_prop']
fudge_prop = kwargs['fudge_prop']
Mstar = M0
SFR = np.repeat(-999., len(M0))
tQ = tQ0
Mq = MQ0
t00 = t0.min() # earliest starting time (cosmic time of ancestor snapshot)
t_evol = np.arange(t00, tf+t_step, t_step)
t_evol[-1] = tf
qf = Fq()
Fq_anal = qf.model
# Mass bins
M_bins = np.arange(6.0, 13., 0.5)
M_mid = 0.5 * (M_bins[:-1] + M_bins[1:])
for tt in t_evol:
print 't_cosmic = ', tt
t_one_tstep = time.time()
within = np.where(t0 <= tt) # tsnap_genesis <= t
sf_within = np.where((t0 <= tt) & (tQ == 999.))[0] # Not quenching SF galaxies
Nsf_0 = len(sf_within)
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t0_snap = np.abs(t_snap-tt).argmin() - 1
Msham_q0 = ancestor_Mq[:, closest_t0_snap]
M_t0_sample = np.concatenate([Mstar[within], Msham_q0[np.where(Msham_q0 > 0.)]])
P_sf = np.random.uniform(0., 1., Nsf_0)
# Initial estimate of quenching probability given by dFq/dt
# P_q = (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
Ng0, dum = np.histogram(M_t0_sample, bins=M_bins)
Nsf0, dum = np.histogram(Mstar[sf_within], bins=M_bins)
P_q_arr = Ng0/Nsf0 * dFqdt(M_mid, tt + 0.5 * t_step, lit=fq_prop['name']) * t_step
P_q_arr[np.where(Nsf0 == 0)] = 0.
print 'M* : ', M_mid[-6:]
print 'P_q : ', P_q_arr[-6:]
Pq_M = interpolate.interp1d(M_mid, P_q_arr, kind='linear')
P_q = Pq_M(Mstar[sf_within])
q_ing = np.where(P_q > P_sf)
Nqing0 = len(q_ing[0])
# assign them quenching times
tQ_tmp = tQ
tQ_tmp[sf_within[q_ing]] = np.random.uniform(low=tt, high=tt+t_step, size=Nqing0)
kwargs_sfr = {
't_init': t0[within],
't_q': tQ_tmp[within],
'M_q': Mq[within],
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'sfms_prop': sfms_prop,
'indices': within
}
M_evol, sfr_evol, Mq_evol = M_integrate(Mstar[within], tt, tt+t_step, massevol_prop=massevol_prop, kwargs_sfr=kwargs_sfr)
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t_snap = np.abs(t_snap - tt - t_step).argmin() - 1
Msham_qf = ancestor_Mq[:, closest_t_snap]
M_tf_sample = np.concatenate([M_evol, Msham_qf])
# dPQ correction to the quenching to account for change in Ng(M*,t)
Ngp, dum = np.histogram(M_tf_sample, bins=M_bins)
dPq = (Ngp - Ng0)/Nsf0.astype('float') * Fq_anal(M_mid, z_of_t(tt + t_step), lit=fq_prop['name'])
dPq[np.where(dPq < 0.)] = 0.
dPq[np.where(Nsf0 == 0)] = 0.
print 'dPq : ', dPq[-6:]
dPq_M = interpolate.interp1d(M_mid, dPq, kind='linear')
P_q += dPq_M(Mstar[sf_within])
fudge_factor = fudge_prop['slope'] * (Mstar[sf_within] - fudge_prop['fidmass']) + fudge_prop['offset']
fudge_factor[np.where(fudge_factor < 1.)] = 1.
P_q *= fudge_factor
q_ing = np.where(P_sf < P_q)
print 'Initial guess ', Nqing0, ' after correction: ', len(q_ing[0]), ' SF galaxies out of ', Nsf_0, ' galaxies start quenching'
print time.time() - t_one_tstep
print '----------------'
# assign them quenching times then actually evolve their stellar masses
tQ[sf_within[q_ing]] = np.random.uniform(low=tt, high=tt+t_step, size=len(q_ing[0]))
kwargs_sfr = {
't_init': t0[within],
't_q': tQ[within],
'M_q': Mq[within],
'dutycycle_prop': dutycycle_prop,
'tau_prop': tau_prop,
'sfms_prop': sfms_prop,
'indices': within
}
M_evol, sfr_evol, Mq_evol = M_integrate(Mstar[within], tt, tt+t_step,
massevol_prop=massevol_prop, kwargs_sfr=kwargs_sfr)
Mstar[within] = M_evol
SFR[within] = sfr_evol
Mq[within] = Mq_evol
if SFR.min() == -999.:
raise ValueError
return Mstar, SFR, tQ
def Plot_dFqdt():
''' Plot the evolution of the derivative of the quiescent fraction as a function of M* and t
for different parameterized models of f_Q
d f_Q / d t
'''
prettyplot()
pretty_colors = prettycolors()
dt = 0.01
M_arr = np.arange(9.0, 12.5, 0.5)
t_arr = np.arange(6.0, 13.0, 0.2)
fig = plt.figure()
sub = fig.add_subplot(111)
for iM, Mstar in enumerate(M_arr):
qf = gp.Fq()
lit = 'wetzelsmooth'
sub.plot(
t_arr,
[gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_arr],
ls='-', lw=3,
c=pretty_colors[iM],
label=r"$\mathtt{M}_* = "+str(round(Mstar,2))+"$"
)
lit = 'wetzel'
sub.plot(
t_arr,
[gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_arr],
ls=':', lw=3,
c=pretty_colors[iM]
)
lit = 'cosmosinterp'
t_blah = np.arange(8.0, 9.6, 0.1)
sub.plot(
t_blah,
[gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_blah],
ls='--', lw=3,
c=pretty_colors[iM]
)
lit = 'cosmosfit'
t_blah = np.arange(8.0, 9.6, 0.1)
sub.plot(
t_blah,
[gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_blah],
ls='-.', lw=3,
c=pretty_colors[iM]
)
sub.set_xlim([t_arr.min(), t_arr.max()])
sub.legend(loc='upper left')
sub.set_ylim([0., 0.2])
#plt.show()
fig_file = ''.join(['figure/test/',
'dfqdt.png'])
fig.savefig(fig_file, bbox_inches='tight')
return None
def _Evol_MshamSFR(self, nsnap_d, allwill, sf_ancestor, q_ancestor):
''' Evolve SFR of the SF galaxies while quenching some of them at the same time.
Notes
-----
* SF galaxies are quenched based on the following prescription:
The number of galaxies that start quenching between t0 and t0+tstep
is determined by first guessing,
N_quenching = N_sf(t0) * (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
'''
tQ0 = self.ancestor.tQ[allwill[sf_ancestor]].copy()
tQ = self.ancestor.tQ[allwill[sf_ancestor]].copy()
Mq = self.ancestor.MQ[allwill[sf_ancestor]].copy()
# earliest starting time (cosmic time of ancestor snapshot)
t0 = self.ancestor.tsnap_genesis[allwill[sf_ancestor]].copy()
t00 = t0.min()
i_low = np.abs(t_snap - np.max(self.tsnap_descendants)).argmin() - 1
i_high = np.abs(t_snap - t00).argmin() + 1
t_evol = t_snap[i_low:i_high][::-1]
qf = Fq()
Fq_anal = qf.model
M_bins = np.arange(5.5, 13., 0.5)
M_mid = 0.5 * (M_bins[:-1] + M_bins[1:])
q_Mshams = self.MshamEvol[allwill[q_ancestor], :].copy()
sf_Mshams = self.MshamEvol[allwill[sf_ancestor], :].copy()
for i_t, tt in enumerate(t_evol[:-1]):
t_step = t_evol[i_t + 1] - tt
if not self.quiet:
print 't_cosmic = ', tt
t_one_tstep = time.time()
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t0_snap = np.abs(t_snap - tt).argmin() - 1
Msham_q0 = q_Mshams[:, closest_t0_snap]
Msham_sf0 = sf_Mshams[:, closest_t0_snap]
within = np.where(
(Msham_sf0 > 0.) & (t0 <= tt)) # tsnap_genesis <= t
sf_within = np.where((Msham_sf0 > 0.) & (t0 <= tt) & (tQ == 999.))[
0] # Not quenching SF galaxies
Nsf_0 = len(sf_within)
M_t0_sample = np.concatenate(
[Msham_sf0[within], Msham_q0[np.where(Msham_q0 > 0.)]])
P_sf = np.random.uniform(0., 1., Nsf_0)
# Initial estimate of quenching probability given by dFq/dt
# P_q = (1/( 1 - fQ(t0) )) * dFq/dt(t0 + 0.5 tstep) * tstep
Ng0, dum = np.histogram(M_t0_sample, bins=M_bins)
Nsf0, dum = np.histogram(Msham_sf0[sf_within], bins=M_bins)
P_q_arr = Ng0/Nsf0 * \
dFqdt(M_mid, tt + 0.5 * t_step, lit=self.fq_prop['name']) * t_step
P_q_arr[np.where(Nsf0 == 0)] = 0.
if not self.quiet:
print 'M* : ', M_mid[-6:]
print 'P_q : ', P_q_arr[-6:]
Pq_M = interpolate.interp1d(M_mid, P_q_arr, kind='linear')
P_q = Pq_M(Msham_sf0[sf_within])
q_ing0 = np.where(P_q > P_sf)
Nqing0 = len(q_ing0[0])
# M_sham of the quiescent galaxies at the closest snapshot.
closest_t1_snap = np.abs(t_snap - t_evol[i_t + 1]).argmin() - 1
Msham_q1 = q_Mshams[:, closest_t1_snap]
Msham_sf1 = sf_Mshams[:, closest_t1_snap]
M_t1_sample = np.concatenate([Msham_sf1[within], Msham_q1])
# dPQ correction to the quenching to account for change in Ng(M*,t)
Ngp, dum = np.histogram(M_t1_sample, bins=M_bins)
dPq = (Ngp - Ng0)/Nsf0.astype('float') * \
Fq_anal(M_mid, z_of_t(tt + t_step), lit=self.fq_prop['name'])
dPq[np.where(dPq < 0.)] = 0.
dPq[np.where(Nsf0 == 0)] = 0.
if not self.quiet:
print 'dPq : ', dPq[-6:]
dPq_M = interpolate.interp1d(M_mid, dPq, kind='linear')
P_q += dPq_M(Msham_sf0[sf_within])
# fudge factor for P_Q
fudge_factor = self.fudge_prop['slope'] * (
Msham_sf0[sf_within] -
self.fudge_prop['fidmass']) + self.fudge_prop['offset']
fudge_factor[np.where(fudge_factor < 1.)] = 1.
P_q *= fudge_factor
if self.printPQ: # store quenching probabilities
Pq_out = {}
Pq_out['mass'] = M_mid
fPQ = self.fudge_prop['slope'] * (M_mid - self.fudge_prop['fidmass']) + \
self.fudge_prop['offset']
fPQ[np.where(fPQ < 1.)] = 1.
Pq_out['Pq'] = fPQ * (P_q_arr + dPq)
Pq_out['Pq_fid'] = P_q_arr + dPq
if self.PQs is None:
self.PQs = {}
self.PQs[str(z_of_t(tt))] = Pq_out
q_ing = np.where(P_sf < P_q)
if not self.quiet:
print 'Initial guess ', Nqing0, ' final: ', len(
q_ing[0]), ' SF gal out of ', Nsf_0, ' start quenching'
print time.time() - t_one_tstep
print '----------------'
# assign them quenching times then actually evolve their stellar masses
tQ[sf_within[q_ing]] = np.random.uniform(low=tt,
high=tt + t_step,
size=len(q_ing[0]))
quenching = np.where((Mq == -999.) & (tQ < 999.) & (tQ > 0.))[0]
Nquenching = len(quenching)
closest_tQ_index = np.abs(
np.tile(t_snap, (Nquenching, 1)) -
np.tile(tQ[quenching].reshape(Nquenching, 1),
(1, len(t_snap)))).argmin(axis=1) - 1
Mq[quenching] = sf_Mshams[quenching, closest_tQ_index]
z0 = z_of_t(t0) # initial redshift
gv0 = np.where(tQ0 == 0.)[0] # galaxies that were initial quenching
t_f = self.descendant_dict[str(nsnap_d)].t_cosmic
z_f = self.descendant_dict[str(nsnap_d)].zsnap
closest_tf_snap = np.abs(t_snap - t_f).argmin() - 1
Msham_f = sf_Mshams[:, closest_tf_snap].copy()
q_ed = np.where(t_f > tQ)
tmp_M = Msham_f.copy()
tmp_M[q_ed] = Mq[q_ed].copy()
# log(SFR)_SFMS evolution from t0
logsfr_sfms = sfr_evol.AverageLogSFR_sfms(tmp_M, z0, sfms_prop=self.sfms_prop) + \
sfr_evol.DeltaLogSFR_sfms(z0, z_f, sfms_prop=self.sfms_prop)
# log(SFR)_duty cycle evolution from t0 to tQ
logsfr_sfduty = sfr_evol.DeltaLogSFR_dutycycle(
t0, t_f, t_q=tQ, dutycycle_prop=self.dutycycle_prop)
logsfr_quench = sfr_evol.DeltaLogSFR_quenching(tQ,
t_f,
M_q=Mq,
tau_prop=self.tau_prop)
logSFR = logsfr_sfms + logsfr_quench + logsfr_sfduty
# correct for the galaxies that were originally in the green valley.
# ultimately doesn't make a difference in the outcome, but to be meticulous
logSFR[gv0] = (self.ancestor.sfr[allwill[sf_ancestor]])[gv0] + \
sfr_evol.DeltaLogSFR_sfms(z0[gv0], z_f, sfms_prop=self.sfms_prop) + \
sfr_evol.DeltaLogSFR_quenching(
np.repeat(self.ancestor.t_cosmic, len(gv0)),
t_f, M_q=Mq[gv0], tau_prop=self.tau_prop)
return logSFR, tQ