def DataSummary(Mrcut=18, observables=['ssfr']):
''' Summary statistics of the data. In our case that is the
SSFR distribution of the SDSS group catalog.
'''
obvs = []
if 'ssfr' in observables:
# Group Catalog object
groupcat = GroupCat(Mrcut=Mrcut, position='central')
# SSFR distribution of group catalog
bins, dist = groupcat.Ssfr()
obvs.append([np.array(bins), np.array(dist)])
if 'fqz03' in observables:
qfrac = Fq()
M_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_mid = 0.5 * (M_bin[:-1] + M_bin[1:])
fq_model = qfrac.model(M_mid, 0.3412, lit='wetzel')
obvs.append([M_mid, fq_model])
if 'fqz_multi' in observables:
qfrac = Fq()
M_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_mid = 0.5 * (M_bin[:-1] + M_bin[1:])
fq_out = [M_mid]
for zz in [0.0502, 0.1581, 0.3412, 1.0833]:
fq_model = qfrac.model(M_mid, zz, lit='wetzel')
fq_out += [fq_model]
obvs.append(fq_out)
if len(observables) == 1:
obvs = obvs[0]
return obvs
def _getPq(ancestor, descendant, succession=None, will=None, pq_prop=None, sfr_prop=None):
'''' Calculate the quenching probability for Star-Forming ancestor galaxies
in the ancestor CGPop object. The quenching probability is calculated using
P_Q = ( f_Q(Mf,zf) - f_Q(M0,z0) ) / (1 - f_Q(M0,z0)) + Offset_P_Q
where Offset_P_Q is a fudge factor dictated by nuisance parameters described
by pq_prop.
Parameters
----------
pq_prop : dict
Quenching properties dictionary. Keywords are 'slope' and 'yint' which
describes the offset to the quenchign probability. The quenchign probability
is fudged around to match the observed quenching fraction at z_final:
fQ(M*, z_final).
Returns
-------
P_q : array
Array that specifies the quenching probabilities for the SF ancestor
galaxies.
'''
z_final = descendant.zsnap # z_final of evolution
# SF ancestors
sf_ancestors = np.where(ancestor.sfr_class[will] == 'star-forming')[0]
P_q_offset = pq_prop['slope'] * (descendant.mass[succession[sf_ancestors]] - 9.5) + pq_prop['yint']
# fQ(M*, z_final)
fq_obj = Fq()
fqf = fq_obj.model(descendant.mass[succession[sf_ancestors]],
z_final,
lit = sfr_prop['fq']['name'])
# fQ(M*, z_initial), which is calculated using mass and redshifts when
# the host subhalo passes the M* threshold.
fq0 = fq_obj.model(descendant.mass_genesis[succession[sf_ancestors]],
descendant.zsnap_genesis[succession[sf_ancestors]],
lit=sfr_prop['fq']['name'])
notallquench = np.where(fq0 < 1.0)
P_q = np.repeat(1.0, len(sf_ancestors))
P_q[notallquench] = (fqf[notallquench] - fq0[notallquench] ) / (1.0 - fq0[notallquench])
P_q += P_q_offset
return P_q
def Plot_fQcen_SDSS():
''' Compare the quiescent fraction of SDSS from the *corrected* SDSS fQ^cen
from Tinker et al. (2013) versus the Wetzel et al. (2013) parameterization.
'''
# mass binnning we impose
m_low = np.array([9.5, 10., 10.5, 11., 11.5])
m_high = np.array([10., 10.5, 11., 11.5, 12.0])
m_mid = 0.5 * (m_low + m_high)
# SDSS
fq_file = ''.join(
['dat/observations/cosmos_fq/', 'fcen_red_sdss_scatter.dat'])
m_sdss, fqcen_sdss, N_sdss = np.loadtxt(fq_file,
unpack=True,
usecols=[0, 1, 2])
fqcen_sdss_rebin = []
for im, m_mid_i in enumerate(m_mid):
sdss_mbin = np.where((m_sdss >= m_low[im]) & (m_sdss < m_high[im]))
fqcen_sdss_rebin.append(
np.sum(fqcen_sdss[sdss_mbin] * N_sdss[sdss_mbin].astype('float')) /
np.sum(N_sdss[sdss_mbin].astype('float')))
fqcen_sdss_rebin = np.array(fqcen_sdss_rebin)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
qf = Fq()
fqcen_model = qf.model(m_mid, 0.05, lit='cosmos_tinker')
sub.plot(m_mid,
fqcen_model,
c=pretty_colors[3],
lw=3,
label=r'Wetzel et al. (2013) fit')
sub.scatter(m_mid,
fqcen_sdss_rebin,
color=pretty_colors[0],
lw=0,
s=40,
label=r'Tinker et al. (2013)')
sub.set_xlim([9.0, 12.0])
sub.set_xlabel(r'$\mathtt{log\;M_*}$', fontsize=25)
sub.set_ylim([0.0, 1.0])
sub.set_ylabel(r'$\mathtt{f_Q^{cen}}$', fontsize=25)
sub.legend(loc='upper left', scatterpoints=1, markerscale=3)
fig_file = ''.join(['figure/test/', 'Fq_central_SDSS.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def model(self, fq_prop={'name': 'wetzelsmooth'}, z=None, **mkwargs):
''' Plot the model Parameterized queiscent fraction as a function
of stellar mass
'''
if z is None:
if self.z is None:
raise ValeuError
else:
redshift = self.z
else:
redshift = z
self.z = z
if self.kwargs == {}:
kwargs = mkwargs.copy()
else:
kwargs = (self.kwargs).copy()
kwargs.update(mkwargs)
if 'label' in kwargs and kwargs['label'] is not None:
fq_label = kwargs['label']
else:
fq_label = fq_prop['name']+'; z = '+str(redshift)
if 'line_color' in kwargs:
line_color = kwargs['line_color']
else:
line_color = 'black'
if 'line_style' in kwargs:
line_style = kwargs['line_style']
else:
line_style = '-'
if 'lw' in kwargs:
line_width = kwargs['lw']
else:
line_width = 4
# parameterized fq
fq_obj = Fq()
fq = fq_obj.model(self.masses, self.z, lit=fq_prop['name'])
self.subs.plot(
self.masses, fq,
color = line_color,
lw = 4,
ls = '--',
label = fq_label
)
return None
def Plot_fQcen_SDSS():
''' Compare the quiescent fraction of SDSS from the *corrected* SDSS fQ^cen
from Tinker et al. (2013) versus the Wetzel et al. (2013) parameterization.
'''
# mass binnning we impose
m_low = np.array([9.5, 10., 10.5, 11., 11.5])
m_high = np.array([10., 10.5, 11., 11.5, 12.0])
m_mid = 0.5 * (m_low + m_high)
# SDSS
fq_file = ''.join(['dat/observations/cosmos_fq/', 'fcen_red_sdss_scatter.dat'])
m_sdss, fqcen_sdss, N_sdss = np.loadtxt(fq_file, unpack=True, usecols=[0,1,2])
fqcen_sdss_rebin = []
for im, m_mid_i in enumerate(m_mid):
sdss_mbin = np.where(
(m_sdss >= m_low[im]) &
(m_sdss < m_high[im]))
fqcen_sdss_rebin.append(
np.sum(fqcen_sdss[sdss_mbin] * N_sdss[sdss_mbin].astype('float'))/np.sum(N_sdss[sdss_mbin].astype('float'))
)
fqcen_sdss_rebin = np.array(fqcen_sdss_rebin)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
qf = Fq()
fqcen_model = qf.model(m_mid, 0.05, lit='cosmos_tinker')
sub.plot(m_mid, fqcen_model,
c=pretty_colors[3], lw=3, label=r'Wetzel et al. (2013) fit')
sub.scatter(m_mid, fqcen_sdss_rebin,
color=pretty_colors[0], lw=0, s=40, label=r'Tinker et al. (2013)')
sub.set_xlim([9.0, 12.0])
sub.set_xlabel(r'$\mathtt{log\;M_*}$', fontsize=25)
sub.set_ylim([0.0, 1.0])
sub.set_ylabel(r'$\mathtt{f_Q^{cen}}$', fontsize=25)
sub.legend(loc='upper left', scatterpoints=1, markerscale=3)
fig_file = ''.join(['figure/test/',
'Fq_central_SDSS.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def DataSummary(Mrcut=18, observables=['ssfr']):
''' Summary statistics of the data. In our case that is the
SSFR distribution of the SDSS group catalog.
'''
obvs = []
if 'ssfr' in observables:
# Group Catalog object
groupcat = GroupCat(Mrcut=Mrcut, position='central')
# SSFR distribution of group catalog
bins, dist = groupcat.Ssfr()
obvs.append([np.array(bins), np.array(dist)])
if 'fqz03' in observables:
qfrac = Fq()
M_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_mid = 0.5 * (M_bin[:-1] + M_bin[1:])
fq_model = qfrac.model(M_mid, 0.3412, lit='wetzel')
obvs.append([M_mid, fq_model])
if len(observables) == 1:
obvs = obvs[0]
return obvs
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 AssignSFR(mass,
redshift,
sfr_prop=None,
ancestor=None,
descendant=None,
quiet=True):
''' Assign star-forming properties based on the input mass and redshift.
Return sfr_class (star-forming or quiescent), SFR, and sSFR given a set
of masses and redshifts.
Parameters
----------
mass : array
Array of galaxy stellar mass
redshift : array
Array of corresponding galaxy redshift
sfr_prop : dictionary
Dictionary that specifies the star-forming properties.
Notes
-----
* Takes ~1.5 seconds for assign_sfr_ancestor in lineage object
'''
if sfr_prop is None:
raise ValueError('Specify SFR Properties dictionary')
# Dictionaries that specify fQ, SFMS, f_GV
fq_dict = sfr_prop['fq']
sfms_dict = sfr_prop['sfms']
gv_dict = sfr_prop['gv']
if 'subhalogrowth' in sfr_prop.keys():
if descendant is None:
raise ValueError
if ancestor is None:
raise ValueError
for key in descendant.__dict__.keys():
if 'ancestor' in key:
ancestor_index = getattr(descendant, key)
ngal = len(mass) # Ngal
sfr_class = np.repeat('', ngal).astype('|S16')
sfr = np.repeat(-999., ngal)
ssfr = np.repeat(-999., ngal)
delta_sfr = np.repeat(-999., ngal)
avg_sfr = np.repeat(-999., ngal)
tQ = np.repeat(999., ngal)
MQ = np.repeat(-999., ngal)
qfrac = Fq() # Fq object
M_bins = np.arange(6.0, 13., 0.5) # mass bin
M_mid = 0.5 * (M_bins[:-1] + M_bins[1:])
# For z = z_ancestor
massive = np.where((mass > 0.0) & (redshift == redshift.max()))[0]
ngal = len(massive)
ngal_M, dum = np.histogram(mass[massive], bins=M_bins)
np.random.seed()
rand = np.random.uniform(0., 1., ngal)
# f_GV(M*) Green valley fraction
gvfrac = gv_dict['slope'] * (mass[massive] -
gv_dict['fidmass']) + gv_dict['offset']
greenvalley = np.where(rand < gvfrac)[0]
ngal_green = len(greenvalley)
ngal_green_M, dum = np.histogram(mass[massive[greenvalley]], bins=M_bins)
if not quiet:
print ngal_green, ' green valley galaxies out of ', ngal, ' galaxies'
sfr_class[
massive[greenvalley]] = 'star-forming' # classified as star-forming
ssfr_q_peak = AverageLogSSFR_q_peak(
mass[massive[greenvalley]]) # Q peak SSFR value
ssfr_sf_peak = AverageLogSFR_sfms(
mass[massive[greenvalley]], # SF peak SSFR value
redshift[massive[greenvalley]],
sfms_prop=sfms_dict) - mass[massive[greenvalley]]
ssfr[massive[greenvalley]] = np.random.uniform(ssfr_q_peak, ssfr_sf_peak,
ngal_green)
sfr[massive[greenvalley]] = ssfr[massive[greenvalley]] + mass[
massive[greenvalley]]
tQ[massive[greenvalley]] = Util.get_tsnap(
redshift.max()) # quenching cosmic time
MQ[massive[greenvalley]] = mass[massive[greenvalley]]
# some green valley galaxies will be classified in observations as
# quiescent or star-forming.
green_class = qfrac.Classify(mass[massive[greenvalley]],
sfr[massive[greenvalley]],
redshift[massive[greenvalley]],
sfms_prop=sfms_dict)
GQ = np.where(green_class == 'quiescent')
ngal_GQ = len(GQ[0])
ngal_GQ_M, dum = np.histogram(mass[massive[greenvalley[GQ]]], bins=M_bins)
# f_Q(M*, z) for each massive galaxy
fq_M = qfrac.model(M_mid, redshift.max(), lit=fq_dict['name'])
fq_prime_arr = (ngal_M * fq_M - ngal_GQ_M) / (ngal_M - ngal_green_M)
fq_prime_arr[np.where(ngal_M == 0)] = 0.
fq_prime_M = interpolate.interp1d(M_mid, fq_prime_arr, kind='linear')
# assign SFR to the not so green valley
notgreenvalley = np.where(rand >= gvfrac)[0]
ngal_notgreenvalley = len(notgreenvalley)
rand_notgreen = np.random.uniform(0., 1., ngal_notgreenvalley)
fq_prime = fq_prime_M(mass[massive[notgreenvalley]]) # fQ'
lowz = np.where((mass > 0.0) & (redshift != redshift.max()))[0]
ngal_lowz = len(lowz)
rand_lowz = np.random.uniform(0., 1., ngal_lowz)
fq_lowz = qfrac.model(mass[lowz], redshift[lowz], lit=fq_dict['name'])
quiescent = np.concatenate([
massive[notgreenvalley[np.where(rand_notgreen < fq_prime)]],
lowz[np.where(rand_lowz < fq_lowz)]
])
starforming = np.concatenate([
massive[notgreenvalley[np.where(rand_notgreen >= fq_prime)]],
lowz[np.where(rand_lowz >= fq_lowz)]
])
ngal_q = len(quiescent)
ngal_sf = len(starforming)
# Assign SFR to quiescent galaxies
sfr_class[quiescent] = 'quiescent'
mu_q_ssfr = AverageLogSSFR_q_peak(mass[quiescent])
sig_q_ssfr = ScatterLogSSFR_q_peak(mass[quiescent])
ssfr[quiescent] = sig_q_ssfr * np.random.randn(ngal_q) + mu_q_ssfr
sfr[quiescent] = ssfr[quiescent] + mass[quiescent]
# Assign SFR to star-forming galaxies
sfr_class[starforming] = 'star-forming'
mu_sf_sfr = AverageLogSFR_sfms(mass[starforming],
redshift[starforming],
sfms_prop=sfms_dict)
sigma_sf_sfr = ScatterLogSFR_sfms(mass[starforming],
redshift[starforming],
sfms_prop=sfms_dict)
avg_sfr[starforming] = mu_sf_sfr
delta_sfr[starforming] = sigma_sf_sfr * np.random.randn(ngal_sf)
sfr[starforming] = mu_sf_sfr + delta_sfr[starforming]
ssfr[starforming] = sfr[starforming] - mass[starforming]
if 'subhalogrowth' in sfr_prop.keys():
# if SFR assignment is dependent on subhalo growth,
# loop through mass bins and assign SFR by abundance matching the
# SFR to the Delta M*_sham.
mass_range = np.arange(mass[starforming].min(),
mass[starforming].max(), 0.1)
m_low = mass_range[:-1]
m_high = mass_range[1:]
for im in range(len(m_low)):
sf_mass_bin = np.where((mass[starforming] > m_low[im])
& (mass[starforming] <= m_high[im]))[0]
succession, will = Util.intersection_index(
ancestor_index, ancestor.snap_index[starforming])
deltaM = descendant.mass[succession] - mass[starforming[will]]
dM_sort_index = np.argsort(deltaM)
deltaSFR = sigma_sf_sfr * np.random.randn(len(deltaM))
dSFR_sort_index = np.argsort(deltaSFR)
# abundance matched.
delta_sfr[starforming[
will[dM_sort_index]]] = deltaSFR[dSFR_sort_index]
sfr[starforming] = mu_sf_sfr + delta_sfr[starforming]
ssfr[starforming] = sfr[starforming] - mass[starforming]
return [sfr_class, sfr, ssfr, delta_sfr, avg_sfr, tQ, MQ]
def AssignCenSFR(tf, abcrun=None, prior_name=None):
''' Given SGPop object, assign SFRs at infalling time based on the
median ABC Run of the Central Galaxy evolution
'''
if abcrun is None:
raise ValueError
if prior_name is None:
raise ValueError
abcinh = ABCInherit(tf, abcrun=abcrun, prior_name=prior_name)
inh = abcinh.PickleLoad('all')
sg_obj = SGPop()
sg_obj.ImportSubhalo(subhalo_prop=abcinh.sim_kwargs['subhalo_prop'])
sg_obj.sfms_prop = abcinh.sim_kwargs['sfr_prop']['sfms']
sg_obj.abcrun = abcrun
sg_obj.prior_name = prior_name
sg_obj.first_infall_sfr = np.repeat(-999., len(sg_obj.first_infall_nsnap))
for i_snap in range(2, abcinh.sim_kwargs['nsnap_ancestor']):
infall_now = np.where(
(sg_obj.first_infall_nsnap == i_snap) &
(sg_obj.first_infall_mass > 0.))[0]
# satellite galaxies that end up massive but have 0. infalling mass
#massive_infall_zero =np.where(
# (sg_obj.first_infall_mass[infall_now] == 0.) &
# (sg_obj.mass[infall_now] > 0.)
# )
des_snap = inh[str(i_snap)]
m_bins = np.arange(des_snap.mass.min()-0.1, des_snap.mass.max()+0.2, 0.2)
for i_m in range(len(m_bins)-1):
in_massbin = np.where(
(des_snap.mass >= m_bins[i_m]) &
(des_snap.mass < m_bins[i_m+1]))
infall_massbin = np.where(
(sg_obj.first_infall_nsnap == i_snap) &
(sg_obj.first_infall_mass >= m_bins[i_m]) &
(sg_obj.first_infall_mass < m_bins[i_m+1]))
if len(infall_massbin[0]) == 0:
continue
des_ssfrs = des_snap.sfr[in_massbin] - des_snap.mass[in_massbin]
pdf_ssfr_i, ssfr_binedges = np.histogram(des_ssfrs,
bins=np.arange(-13.0, -8.0, 0.2), normed=True)
cdf_ssfr_i = np.zeros(len(ssfr_binedges))
cdf_ssfr_i[1:] = np.cumsum(pdf_ssfr_i * np.diff(ssfr_binedges))
cdf_interp = interp1d(cdf_ssfr_i, ssfr_binedges, kind='linear')
rand_i = np.random.uniform(size=len(infall_massbin[0]))
sg_obj.first_infall_sfr[infall_massbin] = cdf_interp(rand_i) + \
sg_obj.first_infall_mass[infall_massbin]
# assign SFR to galaxies that fell in before nsnap = 15
old_infall = np.where(
(sg_obj.first_infall_nsnap >= abcinh.sim_kwargs['nsnap_ancestor']) &
(sg_obj.first_infall_mass > 0.))[0]
Pq = np.random.uniform(size=len(old_infall))
qfrac = Fq()
fq_oldinfall = qfrac.model(sg_obj.first_infall_mass[old_infall], sg_obj.first_infall_z[old_infall], lit='wetzel')
# star-forming
sfing = np.where(Pq > fq_oldinfall)
sig_sfms = sfr_evol.ScatterLogSFR_sfms(
sg_obj.first_infall_mass[old_infall[sfing]],
sg_obj.first_infall_z[old_infall[sfing]],
sfms_prop=sg_obj.sfms_prop)
mu_sfms = sfr_evol.AverageLogSFR_sfms(
sg_obj.first_infall_mass[old_infall[sfing]],
sg_obj.first_infall_z[old_infall[sfing]],
sfms_prop=sg_obj.sfms_prop)
sg_obj.first_infall_sfr[old_infall[sfing]] = mu_sfms + np.random.randn(len(sfing[0])) * sig_sfms
# quiescent
qed = np.where(Pq <= fq_oldinfall)
mu_ssfr_q = sfr_evol.AverageLogSSFR_q_peak(sg_obj.first_infall_mass[old_infall[qed]])
sig_ssfr_q = sfr_evol.ScatterLogSSFR_q_peak(sg_obj.first_infall_mass[old_infall[qed]])
ssfr_q = mu_ssfr_q + np.random.randn(len(qed[0])) * sig_ssfr_q
sg_obj.first_infall_sfr[old_infall[qed]] = ssfr_q + sg_obj.first_infall_mass[old_infall[qed]]
return sg_obj
def QAplot(descendant, sfinh_kwargs, fig_name=None, **kwargs):
''' Given galpop object and the SF Inheritance parametes,
plot its SMF,
'''
# Mass bins
mass_bins = np.arange(7., 12.5, 0.5)
mass_bin_low = mass_bins[:-1]
mass_bin_high = mass_bins[1:]
plt.close()
prettyplot()
fig = plt.figure(1, figsize=[20,12])
for i_sub in range(1,6):
sub_i = fig.add_subplot(2,3,i_sub)
if i_sub == 1: # SMF
mf = SMF()
mass, phi = mf.Obj(descendant)
sub_i.plot(mass, phi, lw=4, c='r', label=r'Simulated')
censub = CentralSubhalos()
censub.Read(descendant.nsnap,
scatter=sfinh_kwargs['subhalo_prop']['scatter'],
source=sfinh_kwargs['subhalo_prop']['source'],
nsnap_ancestor=sfinh_kwargs['nsnap_ancestor'])
mass, phi = mf._smf(censub.mass)
sub_i.plot(mass, phi, c='k', lw=4, ls='--', label='Central Subhalos')
sub_i.set_ylim([10**-5, 10**-1])
sub_i.set_xlim([7.5, 12.0])
plt.xticks([8., 9., 10., 11., 12.])
sub_i.set_yscale('log')
# x,y labels
sub_i.set_ylabel(r'Stellar Mass ($\mathtt{log\; M_*}$)', fontsize=20)
sub_i.set_ylabel(r'Stellar Mass Function $\mathtt{\Phi}$', fontsize=20)
sub_i.legend(loc='upper right', frameon=False)
elif i_sub == 2: # SFMS
# SMF composition based on their initial mass at nsnap_ancestor
# random subsample
n_tot = len(descendant.mass)
r_subsample = np.random.choice(n_tot, size=100000)
sub_i.scatter(descendant.mass[r_subsample], descendant.sfr[r_subsample], color='b', s=0.5)
qfrac = Fq()
m_arr = np.arange(9.0, 12.5, 0.5)
sub_i.plot(
m_arr,
qfrac.SFRcut(m_arr, descendant.zsnap, sfms_prop=(sfinh_kwargs['sfr_prop'])['sfms']),
c='k', ls='--', lw=4)
sub_i.text(10.5, -4., r'$\mathtt{z='+str(descendant.zsnap)+'}$', fontsize=25)
sub_i.set_xlim([9.0, 12.0])
sub_i.set_ylim([-5.0, 2.0])
sub_i.set_ylabel(r'Stellar Mass ($\mathtt{log\; M_*}$)', fontsize=20)
sub_i.set_ylabel(r'$\mathtt{log\;SFR}$', fontsize=20)
elif i_sub == 3: #SMHM
#corner.hist2d(descendant.halo_mass, descendant.mass, ax=sub_i, c='b', fill_contours=True, smooth=1.0)
n_tot = len(descendant.mass)
r_subsample = np.random.choice(n_tot, size=100000)
sub_i.scatter(descendant.halo_mass[r_subsample], descendant.mass[r_subsample], color='b', s=0.5)
stellarmass = descendant.mass
halomass = descendant.halo_mass
mbin = np.arange(halomass.min(), halomass.max(), 0.25)
mlow = mbin[:-1]
mhigh = mbin[1:]
muMstar = np.zeros(len(mlow))
sigMstar = np.zeros(len(mlow))
for im in range(len(mlow)):
mbin = np.where((halomass > mlow[im]) & (halomass <= mhigh[im]))
muMstar[im] = np.mean(stellarmass[mbin])
sigMstar[im] = np.std(stellarmass[mbin])
sub_i.errorbar(0.5*(mlow+mhigh), muMstar, yerr=sigMstar, color='k', lw=3, fmt='o', capthick=2)
sub_i.set_ylim([9.0, 12.0])
sub_i.set_xlim([10.0, 15.0])
sub_i.set_ylabel(r'Stellar Mass ($\mathtt{log\; M_*}$)', fontsize=20)
sub_i.set_xlabel(r'Halo Mass ($\mathtt{log\;M_{Halo}}$)', fontsize=20)
elif i_sub == 4: # Fq
sfq = qfrac.Classify(descendant.mass, descendant.sfr, descendant.zsnap,
sfms_prop=descendant.sfr_prop['sfms'])
gc = GroupCat(Mrcut=18, position='central')
gc.Read()
gc_sfq = qfrac.Classify(gc.mass, gc.sfr, np.mean(gc.z),
sfms_prop=descendant.sfr_prop['sfms'])
#sub_i.plot(mass, fq, color=pretty_colors[descendant.nsnap], lw=3, ls='--',
# label=r'$\mathtt{z =} '+str(descendant.zsnap)+'$')
M_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_low = M_bin[:-1]
M_high = M_bin[1:]
M_mid = 0.5 * (M_low + M_high)
fq = np.zeros(len(M_low))
gc_fq = np.zeros(len(M_low))
for i_m in xrange(len(M_low)):
mlim = np.where((descendant.mass > M_low[i_m]) & (descendant.mass <= M_high[i_m]))
gc_mlim = np.where((gc.mass > M_low[i_m]) & (gc.mass <= M_high[i_m]))
ngal = np.float(len(mlim[0]))
gc_ngal = np.float(len(gc_mlim[0]))
if ngal != 0: # no galaxy in mass bin
ngal_q = np.float(len(np.where(sfq[mlim] == 'quiescent')[0]))
fq[i_m] = ngal_q/ngal
if gc_ngal != 0:
gc_ngal_q = np.float(len(np.where(gc_sfq[gc_mlim] == 'quiescent')[0]))
gc_fq[i_m] = gc_ngal_q/gc_ngal
sub_i.plot(M_mid, fq, color='r', lw=3, label=r'Simulated')
# fQ of the Group Catalog
sub_i.scatter(M_mid, gc_fq, color='k', s=100, lw=0, label='Group Catalog')
# fQ of the model (input) fQ
fq_model = qfrac.model(M_bin, descendant.zsnap, lit=sfinh_kwargs['sfr_prop']['fq']['name'])
sub_i.plot(M_bin, fq_model,
color='k', lw=4, ls='--',
label=sfinh_kwargs['sfr_prop']['fq']['name'].replace('_', ' ').title()
)
sub_i.set_xlim([9.0, 12.0])
sub_i.set_ylim([0.0, 1.0])
sub_i.set_ylabel(r'Stellar Mass ($\mathtt{log\; M_*}$)', fontsize=20)
sub_i.set_ylabel(r'Quiescent Fraction $\mathtt{f_Q}$', fontsize=20)
sub_i.legend(loc='upper left', frameon=False, scatterpoints=1, markerscale=0.75)
elif i_sub == 5: # Tau_Q(M*)
mass_bin = np.arange(9.0, 12.0, 0.1)
tau_m = getTauQ(mass_bin, tau_prop=sfinh_kwargs['evol_prop']['tau'])
sub_i.plot(10**mass_bin, tau_m, color='r', lw=4, label='Simulated')
if 'taus' in kwargs:
tau_slopes, tau_offsets = kwargs['taus']
i_tau_ms = np.zeros((len(mass_bin), len(tau_slopes)))
for i_tau in range(len(tau_slopes)):
i_tau_prop = sfinh_kwargs['evol_prop']['tau'].copy()
i_tau_prop['slope'] = tau_slopes[i_tau]
i_tau_prop['yint'] = tau_offsets[i_tau]
i_tau_m = getTauQ(mass_bin, tau_prop=i_tau_prop)
#sub_i.plot(10**mass_bin, i_tau_m, color='r', alpha=0.1, lw=2)
i_tau_ms[:,i_tau] = i_tau_m
sig_tau = np.zeros(len(mass_bin))
for im in range(len(mass_bin)):
sig_tau[im] = np.std(i_tau_ms[im,:])
sub_i.errorbar(10**mass_bin, tau_m, yerr=sig_tau, color='r', lw=4)
# satellite quenching fraction for comparison
tau_sat = getTauQ(mass_bin, tau_prop={'name': 'satellite'})
sub_i.plot(10**mass_bin, tau_sat, color='black', lw=4, ls='--', label=r'Satellite (Wetzel+ 2013)')
sub_i.set_xlim([10**9.5, 10**11.75])
sub_i.set_ylim([0.0, 2.0])
sub_i.set_xscale('log')
sub_i.set_xlabel(r'Stellar Mass $[\mathtt{M_\odot}]$',fontsize=20)
sub_i.legend(loc='upper right')
sub_i.set_ylabel(r'Quenching Timescales $(\tau_\mathtt{Q})$ [Gyr]',fontsize=20)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
if fig_name is None:
plt.show()
plt.close()
else:
fig.savefig(fig_name, bbox_inches='tight')
plt.close()
return None
def 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 AssignSFR(mass, redshift, sfr_prop=None, ancestor=None, descendant=None, quiet=True):
''' Assign star-forming properties based on the input mass and redshift.
Return sfr_class (star-forming or quiescent), SFR, and sSFR given a set
of masses and redshifts.
Parameters
----------
mass : array
Array of galaxy stellar mass
redshift : array
Array of corresponding galaxy redshift
sfr_prop : dictionary
Dictionary that specifies the star-forming properties.
Notes
-----
* Takes ~1.5 seconds for assign_sfr_ancestor in lineage object
'''
if sfr_prop is None:
raise ValueError('Specify SFR Properties dictionary')
# Dictionaries that specify fQ, SFMS, f_GV
fq_dict = sfr_prop['fq']
sfms_dict = sfr_prop['sfms']
gv_dict = sfr_prop['gv']
if 'subhalogrowth' in sfr_prop.keys():
if descendant is None:
raise ValueError
if ancestor is None:
raise ValueError
for key in descendant.__dict__.keys():
if 'ancestor' in key:
ancestor_index = getattr(descendant, key)
ngal = len(mass) # Ngal
sfr_class= np.repeat('', ngal).astype('|S16')
sfr = np.repeat(-999., ngal)
ssfr = np.repeat(-999., ngal)
delta_sfr = np.repeat(-999., ngal)
avg_sfr = np.repeat(-999., ngal)
tQ = np.repeat(999., ngal)
MQ = np.repeat(-999., ngal)
qfrac = Fq() # Fq object
M_bins = np.arange(6.0, 13., 0.5) # mass bin
M_mid = 0.5 * (M_bins[:-1] + M_bins[1:])
# For z = z_ancestor
massive = np.where((mass > 0.0) & (redshift == redshift.max()))[0]
ngal = len(massive)
ngal_M, dum = np.histogram(mass[massive], bins=M_bins)
np.random.seed()
rand = np.random.uniform(0., 1., ngal)
# f_GV(M*) Green valley fraction
gvfrac = gv_dict['slope'] * (mass[massive] - gv_dict['fidmass']) + gv_dict['offset']
greenvalley = np.where(rand < gvfrac)[0]
ngal_green = len(greenvalley)
ngal_green_M, dum = np.histogram(mass[massive[greenvalley]], bins=M_bins)
if not quiet:
print ngal_green, ' green valley galaxies out of ', ngal, ' galaxies'
sfr_class[massive[greenvalley]] = 'star-forming' # classified as star-forming
ssfr_q_peak = AverageLogSSFR_q_peak(mass[massive[greenvalley]]) # Q peak SSFR value
ssfr_sf_peak = AverageLogSFR_sfms(mass[massive[greenvalley]], # SF peak SSFR value
redshift[massive[greenvalley]],
sfms_prop=sfms_dict) - mass[massive[greenvalley]]
ssfr[massive[greenvalley]] = np.random.uniform(ssfr_q_peak, ssfr_sf_peak, ngal_green)
sfr[massive[greenvalley]] = ssfr[massive[greenvalley]] + mass[massive[greenvalley]]
tQ[massive[greenvalley]] = Util.get_tsnap(redshift.max()) # quenching cosmic time
MQ[massive[greenvalley]] = mass[massive[greenvalley]]
# some green valley galaxies will be classified in observations as
# quiescent or star-forming.
green_class = qfrac.Classify(
mass[massive[greenvalley]],
sfr[massive[greenvalley]],
redshift[massive[greenvalley]],
sfms_prop=sfms_dict)
GQ = np.where(green_class == 'quiescent')
ngal_GQ = len(GQ[0])
ngal_GQ_M, dum = np.histogram(mass[massive[greenvalley[GQ]]], bins=M_bins)
# f_Q(M*, z) for each massive galaxy
fq_M = qfrac.model(M_mid, redshift.max(), lit=fq_dict['name'])
fq_prime_arr = (ngal_M * fq_M - ngal_GQ_M)/(ngal_M - ngal_green_M)
fq_prime_arr[np.where(ngal_M == 0)] = 0.
fq_prime_M = interpolate.interp1d(M_mid, fq_prime_arr, kind='linear')
# assign SFR to the not so green valley
notgreenvalley = np.where(rand >= gvfrac)[0]
ngal_notgreenvalley = len(notgreenvalley)
rand_notgreen = np.random.uniform(0., 1., ngal_notgreenvalley)
fq_prime = fq_prime_M(mass[massive[notgreenvalley]]) # fQ'
lowz = np.where((mass > 0.0) & (redshift != redshift.max()))[0]
ngal_lowz = len(lowz)
rand_lowz = np.random.uniform(0., 1., ngal_lowz)
fq_lowz = qfrac.model(mass[lowz], redshift[lowz], lit=fq_dict['name'])
quiescent = np.concatenate([
massive[notgreenvalley[np.where(rand_notgreen < fq_prime)]],
lowz[np.where(rand_lowz < fq_lowz)]])
starforming = np.concatenate([
massive[notgreenvalley[np.where(rand_notgreen >= fq_prime)]],
lowz[np.where(rand_lowz >= fq_lowz)]])
ngal_q = len(quiescent)
ngal_sf = len(starforming)
# Assign SFR to quiescent galaxies
sfr_class[quiescent] = 'quiescent'
mu_q_ssfr = AverageLogSSFR_q_peak(mass[quiescent])
sig_q_ssfr = ScatterLogSSFR_q_peak(mass[quiescent])
ssfr[quiescent] = sig_q_ssfr * np.random.randn(ngal_q) + mu_q_ssfr
sfr[quiescent] = ssfr[quiescent] + mass[quiescent]
# Assign SFR to star-forming galaxies
sfr_class[starforming] = 'star-forming'
mu_sf_sfr = AverageLogSFR_sfms(
mass[starforming],
redshift[starforming],
sfms_prop=sfms_dict)
sigma_sf_sfr = ScatterLogSFR_sfms(
mass[starforming],
redshift[starforming],
sfms_prop=sfms_dict)
avg_sfr[starforming] = mu_sf_sfr
delta_sfr[starforming] = sigma_sf_sfr * np.random.randn(ngal_sf)
sfr[starforming] = mu_sf_sfr + delta_sfr[starforming]
ssfr[starforming] = sfr[starforming] - mass[starforming]
if 'subhalogrowth' in sfr_prop.keys():
# if SFR assignment is dependent on subhalo growth,
# loop through mass bins and assign SFR by abundance matching the
# SFR to the Delta M*_sham.
mass_range = np.arange(mass[starforming].min(), mass[starforming].max(), 0.1)
m_low = mass_range[:-1]
m_high = mass_range[1:]
for im in range(len(m_low)):
sf_mass_bin = np.where(
(mass[starforming] > m_low[im]) &
(mass[starforming] <= m_high[im]))[0]
succession, will = Util.intersection_index(
ancestor_index,
ancestor.snap_index[starforming])
deltaM = descendant.mass[succession] - mass[starforming[will]]
dM_sort_index = np.argsort(deltaM)
deltaSFR = sigma_sf_sfr * np.random.randn(len(deltaM))
dSFR_sort_index = np.argsort(deltaSFR)
# abundance matched.
delta_sfr[starforming[will[dM_sort_index]]] = deltaSFR[dSFR_sort_index]
sfr[starforming] = mu_sf_sfr + delta_sfr[starforming]
ssfr[starforming] = sfr[starforming] - mass[starforming]
return [sfr_class, sfr, ssfr, delta_sfr, avg_sfr, tQ, MQ]