def param_sfms(self, nsnap = None, **pltkwargs):
'''
Plot parameterized SFMS
'''
sfr_mstar_z, sig_sfr_mstar_z = get_param_sfr_mstar_z()
if nsnap is None:
if self.cenque_nsnap is not None:
nsnap = self.cenque_nsnap
else:
raise ValueError('nsnap needs to be specified')
if 'justsf' in self.kwargs:
self.sub.plot(
np.arange(8.5, 12.0, 0.1),
sfr_mstar_z(np.arange(8.5, 12.0, 0.1), get_z_nsnap(nsnap)),
c = 'k',
ls = '--',
lw = 3
)
else:
plt.plot(
np.arange(8.5, 12.0, 0.1),
sfr_mstar_z(np.arange(8.5, 12.0, 0.1), get_z_nsnap(nsnap)),
c = 'k',
ls = '--',
lw = 3
)
return None
def param_sfms(self, nsnap=None, **pltkwargs):
'''
Plot parameterized SFMS
'''
sfr_mstar_z, sig_sfr_mstar_z = get_param_sfr_mstar_z()
if nsnap is None:
if self.cenque_nsnap is not None:
nsnap = self.cenque_nsnap
else:
raise ValueError('nsnap needs to be specified')
if 'justsf' in self.kwargs:
self.sub.plot(np.arange(8.5, 12.0, 0.1),
sfr_mstar_z(np.arange(8.5, 12.0, 0.1),
get_z_nsnap(nsnap)),
c='k',
ls='--',
lw=3)
else:
plt.plot(np.arange(8.5, 12.0, 0.1),
sfr_mstar_z(np.arange(8.5, 12.0, 0.1),
get_z_nsnap(nsnap)),
c='k',
ls='--',
lw=3)
return None
def rho_ssfr_cq_evol(
start_nsnap = 13,
final_nsnap = 1,
sf_prop = {'name': 'average'},
fq_prop = {'name': 'wetzelsmooth'},
tau_prop = {'name': 'instant'},
mass_evol = 'sham',
Mrcut=18,
**kwargs
):
""" Compare sSFR distribution of evolved CenQue and
SDSS Group Catalog in the green valley
"""
if Mrcut == 18:
z_med = 0.03
elif Mrcut == 19:
z_med = 0.05
elif Mrcut == 20:
z_med = 0.08
evol_cq_ssfr_bin, evol_cq_ssfr_hist = ssfr_cq_evol(
start_nsnap = start_nsnap,
final_nsnap = final_nsnap,
sf_prop = sf_prop,
fq_prop = fq_prop,
tau_prop = tau_prop,
mass_evol = mass_evol,
**kwargs
)
group_ssfr = Ssfr()
group_ssfr_bin, group_ssfr_hist = group_ssfr.groupcat(Mrcut=Mrcut)
l2_ssfr = 0.0
for i_massbin, massbin in enumerate(group_ssfr.mass_bins):
if not np.array_equal(evol_cq_ssfr_bin[i_massbin], group_ssfr_bin[i_massbin]):
raise ValueError()
# sSFR comparison range
q_ssfr_massbin = np.mean(get_q_ssfr_mean(massbin))
sfr_mstar_z, sig_sfr_mstar_z = get_param_sfr_mstar_z()
sf_ssfr_massbin = sfr_mstar_z(massbin[1], z_med) - massbin[1]
green_range = np.where(
(evol_cq_ssfr_bin[i_massbin] > q_ssfr_massbin) &
(evol_cq_ssfr_bin[i_massbin] < sf_ssfr_massbin)
)
#print np.sum((evol_cq_ssfr_hist[i_massbin][green_range] - group_ssfr_hist[i_massbin][green_range])**2)
l2_ssfr += np.sum((evol_cq_ssfr_hist[i_massbin][green_range] - group_ssfr_hist[i_massbin][green_range])**2)
return l2_ssfr
def rho_ssfr_cq_evol(start_nsnap=13,
final_nsnap=1,
sf_prop={'name': 'average'},
fq_prop={'name': 'wetzelsmooth'},
tau_prop={'name': 'instant'},
mass_evol='sham',
Mrcut=18,
**kwargs):
""" Compare sSFR distribution of evolved CenQue and
SDSS Group Catalog in the green valley
"""
if Mrcut == 18:
z_med = 0.03
elif Mrcut == 19:
z_med = 0.05
elif Mrcut == 20:
z_med = 0.08
evol_cq_ssfr_bin, evol_cq_ssfr_hist = ssfr_cq_evol(start_nsnap=start_nsnap,
final_nsnap=final_nsnap,
sf_prop=sf_prop,
fq_prop=fq_prop,
tau_prop=tau_prop,
mass_evol=mass_evol,
**kwargs)
group_ssfr = Ssfr()
group_ssfr_bin, group_ssfr_hist = group_ssfr.groupcat(Mrcut=Mrcut)
l2_ssfr = 0.0
for i_massbin, massbin in enumerate(group_ssfr.mass_bins):
if not np.array_equal(evol_cq_ssfr_bin[i_massbin],
group_ssfr_bin[i_massbin]):
raise ValueError()
# sSFR comparison range
q_ssfr_massbin = np.mean(get_q_ssfr_mean(massbin))
sfr_mstar_z, sig_sfr_mstar_z = get_param_sfr_mstar_z()
sf_ssfr_massbin = sfr_mstar_z(massbin[1], z_med) - massbin[1]
green_range = np.where((evol_cq_ssfr_bin[i_massbin] > q_ssfr_massbin) &
(evol_cq_ssfr_bin[i_massbin] < sf_ssfr_massbin))
#print np.sum((evol_cq_ssfr_hist[i_massbin][green_range] - group_ssfr_hist[i_massbin][green_range])**2)
l2_ssfr += np.sum((evol_cq_ssfr_hist[i_massbin][green_range] -
group_ssfr_hist[i_massbin][green_range])**2)
return l2_ssfr
def plot_sfms_wetzel_vs_lee():
'''
Plot Wetzel parameterized SFMS and Lee et al. (2015) parameterized SFMS.
'''
# wetzel
wetzel_sfr_mstar_z, wetzel_sig_sfr_mstar_z = get_param_sfr_mstar_z()
# Lee et al. (2015)
z_mid = np.array([0.36, 0.55, 0.70, 0.85, 0.99, 1.19])
logM_lim = np.array([8.50, 9.00, 9.00, 9.30, 9.30, 9.30])
masses = np.arange(7.5, 12.0, 0.1)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
for i_z, z in enumerate(z_mid):
sub.scatter(
masses,
lee_et_al_2015(masses, z),
c = pretty_colors[i_z],
label = r"$\mathtt{z = "+str(round(z, 2))+"}$"
)
sub.plot(
masses,
wetzel_sfr_mstar_z(masses, z),
c = pretty_colors[i_z],
ls = '--',
lw = 3
)
#sub.vlines(logM_lim[i_z], -5.0, 2.0, color='k', linestyle='--', lw=2)
sub.set_xlim([7.0, 12.0])
sub.set_ylim([-3.0, 2.0])
sub.set_xlabel(r'$\mathtt{log\;M_*}$', fontsize=30)
sub.set_ylabel(r'$\mathtt{log\;SFR}$', fontsize=30)
sub.legend(loc='lower right', scatterpoints=1)
fig_file = ''.join([
'figure/',
'qaplot_sfms_wetzel_vs_lee.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.show()
def plot_sfms_wetzel_vs_lee():
'''
Plot Wetzel parameterized SFMS and Lee et al. (2015) parameterized SFMS.
'''
# wetzel
wetzel_sfr_mstar_z, wetzel_sig_sfr_mstar_z = get_param_sfr_mstar_z()
# Lee et al. (2015)
z_mid = np.array([0.36, 0.55, 0.70, 0.85, 0.99, 1.19])
logM_lim = np.array([8.50, 9.00, 9.00, 9.30, 9.30, 9.30])
masses = np.arange(7.5, 12.0, 0.1)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
for i_z, z in enumerate(z_mid):
sub.scatter(masses,
lee_et_al_2015(masses, z),
c=pretty_colors[i_z],
label=r"$\mathtt{z = " + str(round(z, 2)) + "}$")
sub.plot(masses,
wetzel_sfr_mstar_z(masses, z),
c=pretty_colors[i_z],
ls='--',
lw=3)
#sub.vlines(logM_lim[i_z], -5.0, 2.0, color='k', linestyle='--', lw=2)
sub.set_xlim([7.0, 12.0])
sub.set_ylim([-3.0, 2.0])
sub.set_xlabel(r'$\mathtt{log\;M_*}$', fontsize=30)
sub.set_ylabel(r'$\mathtt{log\;SFR}$', fontsize=30)
sub.legend(loc='lower right', scatterpoints=1)
fig_file = ''.join(['figure/', 'qaplot_sfms_wetzel_vs_lee.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.show()
def assign_sfr(cenque,
sf_prop={'name': 'average'},
fq_prop={'name': 'wetzelsmooth'},
quiet=True,
**kwargs):
""" Assign star-formation properties to CenQue object.
The function Goes through mass bins and then classifies unassigned
galaxies into quiescent/star-forming based on quiescent fraction
function.
Then SF properties are assigned to the galaxies. For quiescent
galaxies the sSFR is drawn from a log normal distribution about
some predeterimined mu_sSFR. For star-forming galaxies, SFR is
sampled from a designated SF-MS model.
----------------------------------------------------------------
Parameters
----------------------------------------------------------------
cenque : CenQue class object
----------------------------------------------------------------
Notes
----------------------------------------------------------------
* Re imagine how to set up SFR
"""
# time the code
start_time = time.time()
if cenque.mass == None:
raise ValueError()
if cenque.zsnap == None:
raise ValueError()
if 'sf_prop' not in cenque.__dict__.keys():
cenque.sf_prop = sf_prop
else:
if cenque.sf_prop != sf_prop:
cenque.sf_prop = sf_prop
warnings.warn("SF properties do not match; Replacing SF_prop")
if 'fq_prop' not in cenque.__dict__.keys():
cenque.fq_prop = fq_prop
else:
if cenque.fq_prop != fq_prop:
warnings.warn("fQ properties do not match")
mass_bins = cenque.mass_bins
mass_bin_low = mass_bins.mass_low
mass_bin_mid = mass_bins.mass_mid
mass_bin_high = mass_bins.mass_high
# remove galaxies below the minimum and maximum mass and galaxies without children
within_massbin_with_child = np.where(
(cenque.mass > mass_bins.mass_low.min())
& (cenque.mass <= mass_bins.mass_high.max()) & (cenque.child >= 0))
if (min(cenque.mass) < mass_bins.mass_low.min()) or (max(
cenque.mass) > mass_bins.mass_high.max()):
cenque.sample_trim(within_massbin_with_child)
ngal_tot = len(within_massbin_with_child[0])
for attrib in ['sf_prop', 'fq_prop']:
if attrib not in cenque.metadata:
cenque.metadata.append(attrib)
for attrib in ['gal_type', 'sfr', 'ssfr']:
if attrib not in cenque.data_columns:
cenque.data_columns.append(attrib)
if cenque.gal_type is None:
cenque.gal_type = np.array(['' for i in xrange(ngal_tot)],
dtype='|S16')
cenque.sfr = np.array([-999. for i in xrange(ngal_tot)])
cenque.ssfr = np.array([-999. for i in xrange(ngal_tot)])
# simplest SFR assignment for starforming galaxies. Use mu_SFR(M*, z)
# and randomly sampled normal delta_SFR.
if sf_prop['name'] == 'average':
sfr_mstar_z, sig_sfr_mstar_z = get_param_sfr_mstar_z()
if 'delta_sfr' not in cenque.__dict__.keys():
cenque.delta_sfr = np.array([-999. for i in xrange(ngal_tot)])
if 'avg_sfr' not in cenque.__dict__.keys():
cenque.avg_sfr = np.array([-999. for i in xrange(ngal_tot)])
extra_attr = ['avg_sfr', 'delta_sfr']
elif sf_prop['name'] == 'average_noscatter':
sfr_mstar_z, sig_sfr_mstar_z = get_param_sfr_mstar_z()
if 'delta_sfr' not in cenque.__dict__.keys():
cenque.delta_sfr = np.array([-999. for i in xrange(ngal_tot)])
if 'avg_sfr' not in cenque.__dict__.keys():
cenque.avg_sfr = np.array([-999. for i in xrange(ngal_tot)])
extra_attr = ['avg_sfr', 'delta_sfr']
else:
raise NotImplementedError()
for attrib in extra_attr:
if attrib not in cenque.data_columns:
cenque.data_columns.append(attrib)
# f_Q(M_*,mid, z_snapshot)
qf_massbin = get_fq(mass_bin_mid, cenque.zsnap, lit=fq_prop['name'])
massbin_unassigned = [
np.where((cenque.mass > mass_bin_low[i_m])
& (cenque.mass <= mass_bin_high[i_m])
& (cenque.gal_type == ''))[0]
for i_m in xrange(mass_bins.nbins)
]
# Ngal(M_mid), Ngal,Q(M_mid) and Ngal,SF(M_mid)
ngal_massbin = np.array([x.size for x in massbin_unassigned])
ngal_q_massbin = np.rint(qf_massbin *
ngal_massbin.astype(float)).astype(int)
ngal_sf_massbin = ngal_massbin - ngal_q_massbin
# fail-safe for ngal_q_massbin
if len(np.where(ngal_q_massbin > ngal_massbin)[0]) > 0:
ngal_q_massbin[np.where(
ngal_q_massbin > ngal_massbin)] = ngal_massbin[np.where(
ngal_q_massbin > ngal_massbin)]
for i_m in xrange(mass_bins.nbins):
if len(massbin_unassigned[i_m]) == 0:
continue
begin_loop_time = time.time()
if not quiet:
print mass_bin_low[i_m], ' < M < ', mass_bin_high[i_m]
print 'fQ = ', qf_massbin[i_m], ' Ngal = ', ngal_massbin[i_m]
print 'Ngal,Q = ', ngal_q_massbin[
i_m], ' Ngal,SF = ', ngal_sf_massbin[i_m]
shuffled_massbin_index = np.arange(ngal_massbin[i_m])
np.random.seed()
np.random.shuffle(shuffled_massbin_index)
i_q_end = ngal_q_massbin[i_m]
# Randomly select ngal_q_massbin quiescent galaxies from the
# massbin. Assign them 'quiescent' gal_type and sSFR and SFR
# based on a predetermined gaussian distribution about sSFR.
if ngal_q_massbin[i_m] > 0:
q_massbin = shuffled_massbin_index[:i_q_end]
i_q_massbin = (massbin_unassigned[i_m])[q_massbin]
cenque.gal_type[i_q_massbin] = 'quiescent'
# sample SSFR from log-normal distribution with a preset
# 0.18 dex scatter centered about some predetermined mean
mu_q_ssfr = util.get_q_ssfr_mean(cenque.mass[i_q_massbin])
if len(i_q_massbin) != ngal_q_massbin[i_m]:
print shuffled_massbin_index
print q_massbin
print i_q_massbin
print i_q_end, ngal_q_massbin[i_m]
cenque.ssfr[i_q_massbin] = 0.18 * np.random.randn(
ngal_q_massbin[i_m]) + mu_q_ssfr
cenque.sfr[i_q_massbin] = cenque.ssfr[i_q_massbin] + cenque.mass[
i_q_massbin]
# ngal_sf_massbin starforming galaxies from the massbin. Assign
# them 'star-forming' gal_type and sSFR and SFR in some manner
if ngal_sf_massbin[i_m] > 0:
#sf_massbin = shuffled_massbin_index[i_q_end:i_sf_end]
sf_massbin = shuffled_massbin_index[i_q_end:]
i_sf_massbin = (massbin_unassigned[i_m])[sf_massbin]
cenque.gal_type[i_sf_massbin] = 'star-forming'
# sample SFR
if sf_prop['name'] == 'average':
mu_sf_sfr = sfr_mstar_z(cenque.mass[i_sf_massbin],
cenque.zsnap)
sigma_sf_sfr = sig_sfr_mstar_z(cenque.mass[i_sf_massbin],
cenque.zsnap)
cenque.avg_sfr[i_sf_massbin] = mu_sf_sfr
cenque.delta_sfr[
i_sf_massbin] = sigma_sf_sfr * np.random.randn(
ngal_sf_massbin[i_m])
cenque.sfr[
i_sf_massbin] = mu_sf_sfr + cenque.delta_sfr[i_sf_massbin]
if not quiet:
print 'Starforming galaxies: '
print 'Average(SFR) = ', mu_sf_sfr, ' sigma(SFR) = ', sigma_sf_sfr
elif sf_prop['name'] == 'average_noscatter':
mu_sf_sfr = sfr_mstar_z(cenque.mass[i_sf_massbin],
cenque.zsnap)
sigma_sf_sfr = 0.0
cenque.avg_sfr[i_sf_massbin] = mu_sf_sfr
cenque.delta_sfr[
i_sf_massbin] = sigma_sf_sfr * np.random.randn(
ngal_sf_massbin[i_m])
cenque.sfr[
i_sf_massbin] = mu_sf_sfr + cenque.delta_sfr[i_sf_massbin]
if not quiet:
print 'Starforming galaxies: '
print 'Average(SFR) = ', mu_sf_sfr, ' sigma(SFR) = ', sigma_sf_sfr
else:
raise NotImplementedError
cenque.ssfr[i_sf_massbin] = cenque.sfr[i_sf_massbin] - cenque.mass[
i_sf_massbin]
# double check that SF assign didn't fail anywhere
assign_fail = np.where((cenque.sfr == -999.0) | (cenque.ssfr == -999.0))
if len(assign_fail[0]) > 0:
raise NameError('Function failed!')
if not quiet:
print 'Assign SFR function takes', (time.time() -
start_time) / 60.0, ' minutes'
if 'evol_from' in cenque.cenque_type:
pass
else:
cenque.cenque_type = 'sf_assigned'
return cenque
def test_mass_evol(
mass0,
t0,
tf,
delmass0=0.1,
t_initial=4.0079,
sfrevol_param=[1.0, 1.0],
sfrevol_prop={'name': 'notperiodic'},
massevol_prop={
'name': 'integrated',
'type': 'euler',
'f_retain': 0.6,
't_step': 0.025
},
):
'''
Testing integrated mass evolution
logSFR(z) function is implemented within this module
'''
# spline between z and t_cosmic
z, t = np.loadtxt(Util.snapshottable(), unpack=True, usecols=[2, 3])
z_of_t = interpolate.interp1d(list(reversed(t)),
list(reversed(z)),
kind='cubic')
z0 = z_of_t(t0)
zf = z_of_t(tf)
# average SFR of SF MS
logsfr_mstar_z, sig_logsfr_mstar_z = get_param_sfr_mstar_z()
def logsfr_notquenched(logmass, t_input, **kwargs):
zi = kwargs['z0']
mass_i = kwargs['mass0']
delmass_i = kwargs['deltamass0']
avglogsfr = logsfr_mstar_z(logmass, zi)
#print 'SFMS(', mass0[0], ',', str(zi), ') ', avglogsfr[0]
# Contribution from the SF evolution of the SFMS
logsfr_sfms = sfr_evol.logsfr_sfms_evol(zi, z_of_t(t_input))
# Contribution from the SF Duty cycle
logsfr_sfduty = sfr_evol.logsfr_sfduty_fluct(
t_initial,
t_input,
delta_sfr=delmass_i,
sfrevol_param=sfrevol_param,
**sfrevol_prop)
#print logsfr_sfduty
logsfr_f = avglogsfr + logsfr_sfms + logsfr_sfduty
#print np.max(logsfr_f - logsfr_mstar_z(logmass, z_of_t(t_input))), np.min(logsfr_f - logsfr_mstar_z(logmass, z_of_t(t_input)))
return logsfr_f
sfrkwargs = {'z0': z0, 'mass0': mass0, 'deltamass0': delmass0}
mass_f, sfr_f = mass_evol.integrated(massevol_prop['type'],
logsfr_notquenched,
mass0,
t0,
tf,
f_retain=massevol_prop['f_retain'],
delt=massevol_prop['t_step'],
**sfrkwargs)
#print sfr_f[:3]
#print logsfr_notquenched(mass_f, tf, **sfrkwargs)[:3]
#print logsfr_mstar_z(mass0, zf)[:3]
return mass_f, sfr_f
def plot_integrated_mass_evol(mass0,
t0,
tf,
sfrevol_prop={'name': 'notperiodic'},
massevol_prop={
'name': 'integrated',
'type': 'euler',
'f_retain': 0.6,
't_step': 0.05
},
title=None,
fig_file=None):
'''
Plot of the integrated mass and SFR evolution as a function of cosmic time
'''
if title is None:
raise ValueError
delt = (tf - t0) / 10.0
tfs = np.arange(t0 + delt, tf + delt, delt)
z, t = np.loadtxt(Util.snapshottable(), unpack=True, usecols=[2, 3])
z_of_t = interpolate.interp1d(list(reversed(t)),
list(reversed(z)),
kind='cubic')
# SF MS
logsfr_mstar_z, sig_logsfr_mstar_z = get_param_sfr_mstar_z()
# figure
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(10, 12))
# mass evolution
m_sub = fig.add_subplot(211)
# SFR evolution
sub = fig.add_subplot(212)
delta_masses = np.arange(-0.3, 0.3, 0.05)
#delta_masses = [0.0] # test case
for delmass in delta_masses:
# SFR evolution parameters (for SF duty cycle)
sfrevol_param = sfr_evol.get_sfrevol_param(1, np.array([0]),
**sfrevol_prop)
masses = [mass0]
sfmssfrs = [logsfr_mstar_z(mass0, z_of_t(t0))]
sfrs = [logsfr_mstar_z(mass0, z_of_t(t0)) + delmass]
mass_i_1 = mass0
t_i_1 = t0
for tf_i in tfs:
mass_i, sfr_i = test_mass_evol(mass0,
t0,
tf_i,
delmass0=delmass,
t_initial=t0,
sfrevol_param=sfrevol_param,
sfrevol_prop=sfrevol_prop,
massevol_prop=massevol_prop)
masses.append(mass_i)
sfrs.append(sfr_i)
sfmssfrs.append(logsfr_mstar_z(mass0, z_of_t(tf_i)))
t_i_1 = tf_i
mass_i_1 = mass_i
tfs = np.hstack([t0, tfs])
for i in xrange(len(masses[0])):
if delmass == delta_masses[0]:
m_label = str(mass0[i])
else:
m_label = None
m_sub.plot(tfs,
np.array(masses)[:, i],
c=pretty_colors[i],
lw=3,
alpha=0.5,
label=m_label)
for i in xrange(len(masses[0])):
if delmass == delta_masses[0]:
sfr_label = str(mass0[i])
else:
sfr_label = None
sub.plot(tfs,
np.array(sfrs)[:, i],
c=pretty_colors[i],
lw=2,
alpha=0.5,
label=sfr_label)
sub.plot(tfs, np.array(sfmssfrs)[:, i], c='k', lw=2, ls='--')
for tf_i in tfs:
sub.vlines(tf_i, -1.0, 2.5, color='k', linewidth=1, linestyle=':')
m_sub.set_xlim([t0 - 2.5, tf + 0.5])
m_sub.set_ylim([8.5, 12.5])
m_sub.set_ylabel(r"$\mathtt{log\;M_*}$", fontsize=20)
m_sub.set_title(title)
sub.set_xlim([t0 - 2.5, tf + 0.5])
sub.set_ylim([-2.0, 2.0])
sub.set_xlabel(r"$\mathtt{t_{cosmic}}$", fontsize=20)
sub.set_ylabel(r"$\mathtt{log\;SFR}$", fontsize=20)
sub.legend(loc='upper left')
if fig_file is not None:
figfile = ''.join(['figure/', fig_file])
fig.savefig(figfile, bbox_inches='tight')
#plt.show()
plt.close()
def evolve_onestep(parent_cq, child_cq, predict_step = 3, quiet=False):
""" Evolve CenQue class object by one time step
"""
evo_start = time.time()
mass_bins = child_cq.mass_bins
# remove galaxies below the min and max mass
within_massbin = np.where(
(child_cq.mass > min(mass_bins.mass_low)) &
(child_cq.mass <= max(mass_bins.mass_high))
)
child_cq.sample_trim(within_massbin)
n_child = len(within_massbin[0])
n_parent = len(parent_cq.mass)
# SF and stellar mass properties are added as attributes
# to the CenQue object
child_cq.gal_type = np.array(['' for i in xrange(n_child)], dtype='|S16')
child_cq.sfr = np.array([-999. for i in xrange(n_child)])
child_cq.ssfr = np.array([-999. for i in xrange(n_child)])
child_cq.q_ssfr = np.array([-999. for i in xrange(n_child)])
child_cq.tau = np.array([-999. for i in xrange(n_child)])
child_cq.parent_sfr = np.array([-999. for i in xrange(n_child)])
child_cq.parent_mass = np.array([-999. for i in xrange(n_child)])
child_cq.parent_halo_mass = np.array([-999. for i in xrange(n_child)])
for attrib in ['gal_type', 'sfr', 'ssfr', 'q_ssfr', 'tau',
'parent_sfr', 'parent_mass', 'parent_halo_mass']:
if attrib not in child_cq.data_columns:
child_cq.data_columns.append(attrib)
if child_cq.sf_prop['name'] == 'average':
child_cq.delta_sfr = np.array([-999. for i in range(n_child)])
extra_attr = ['delta_sfr']
else:
raise NotImplementedError()
for attrib in extra_attr:
if attrib not in child_cq.data_columns:
child_cq.data_columns.append(attrib)
# parent children match which assigns indices into dictionaries and then get dictionary values
parents, children = parent_children_match(parent_cq.snap_index, child_cq.parent)
# Deal with parent to children inheritance (children inherit the parents' attributes)
# This step needs to be thought out more.
if not quiet:
inheritance_time = time.time()
child_cq.parent_sfr[children] = parent_cq.sfr[parents]
child_cq.parent_mass[children] = parent_cq.mass[parents]
child_cq.parent_halo_mass[children] = parent_cq.halo_mass[parents]
for attr in child_cq.data_columns:
# Inherit everything but the TreePM and Halo information
if attr not in ('mass', 'halo_mass', 'parent', 'child', 'ilk', 'snap_index', 'pos',
'parent_sfr', 'parent_mass', 'parent_halo_mass'):
try:
parent_attr = getattr(parent_cq, attr)[parents]
getattr(child_cq, attr)[children] = parent_attr
except AttributeError:
if 'evol_from' not in parent_cq.cenque_type:
pass
else:
raise ValueError()
if not quiet:
print 'Inheritance takes ', time.time() - inheritance_time
# Stellar mass evolution of star forming galaxies. Either integrated or
# SHAM masses assigned from TreePM
if child_cq.mass_evol == 'integrated':
# integrated SFR mass evolution of star-forming galaxies
# (only star-forming galaxies)
if kwargs['sfr'] == 'sfr_func':
integrated_mass = util.integrated_mass_rk4(
util.sfr_squarewave, (child_cq.parent_mass)[sf_child_indx],
parent_cq.t_cosmic, child_cq.t_cosmic,
amp = (child_cq.sfr_amp)[sf_child_indx],
freq = (child_cq.sfr_freq)[sf_child_indx],
phase = (child_cq.sfr_phase)[sf_child_indx])
(child_cq.mass)[sf_child_indx] = integrated_mass
elif kwargs['sfr'] == 'sfr_avg':
(child_cq.mass)[sf_child_indx] = util.integrated_mass_rk4(
util.sfr_avg_residual, (child_cq.parent_mass)[sf_child_indx],
parent_cq.t_cosmic, child_cq.t_cosmic,
resid = (child_cq.sfr_resid)[sf_child_indx])
if not silent:
print 'Integrated Mass vs SHAM mass'
print (child_cq.mass)[sf_child_indx] - (child_cq.sham_mass)[sf_child_indx]
# Evolve Quiescent Children.
# Quiecsent galaxies keep the same sSFR while the quiescent galaxy
# masses are assigned by SHAM masses
quiescent_children = np.where(
(child_cq.gal_type[children] == 'quiescent') &
(child_cq.tau[children] == -999.0)
)
q_children = children[quiescent_children]
# correct the SFR in order to preserve SSFR
# this is because the mass evolves through SHAM
child_cq.sfr[q_children] = child_cq.ssfr[q_children] + child_cq.mass[q_children]
# Evolve Star-forming Children
# include doucmentation details
# include doucmentation details
# include doucmentation details
# include doucmentation details
# include doucmentation details
starforming_children = np.where(
child_cq.gal_type[children] == 'star-forming'
)
sf_children = children[starforming_children]
if child_cq.sf_prop['name'] == 'average':
sfr_mstar_z, sig_sfr_mstar_z = get_param_sfr_mstar_z()
child_sfr = sfr_mstar_z(
child_cq.mass[sf_children],
child_cq.zsnap
)
child_cq.sfr[sf_children] = child_sfr + child_cq.delta_sfr[sf_children]
else:
raise NotImplementedError()
child_cq.ssfr[sf_children] = child_cq.sfr[sf_children] - child_cq.mass[sf_children]
# Evolve quenching children by exp(- delta t_cosmic / tau)
quenching_time = time.time()
still_quenching = np.where(child_cq.tau > 0.0)
if len(still_quenching[0]) > 0:
# for galaxies with tau, keep them quenching!
tau_quench = np.log10(
np.exp(
-(child_cq.t_cosmic - parent_cq.t_cosmic) / child_cq.tau[still_quenching]
)
) # SFR quenching amount
child_cq.sfr[still_quenching] = child_cq.parent_sfr[still_quenching] + tau_quench
child_cq.ssfr[still_quenching] = child_cq.sfr[still_quenching] - child_cq.mass[still_quenching]
else:
if 'evol_from' in parent_cq.cenque_type:
if child_cq.tau_prop['name'] != 'instant':
raise ValueError()
if not quiet:
print 'Quenching SF galaxies takes ', time.time() - quenching_time
# numpy.where of mass bins
mass_bin_indices = np.array([
np.where(
(child_cq.mass > mass_bins.mass_low[i_m]) &
(child_cq.mass <= mass_bins.mass_high[i_m]) &
(child_cq.gal_type != '')
)
for i_m in xrange(mass_bins.nbins)
])
# number of galaxies in each of the mass bins
ngal_mass_bin = np.array([
len(mass_bin_indices[i_m][0])
for i_m in xrange(mass_bins.nbins)
])
# f_Q of child CenQue as a function of mass
# f_Q(M_bin)
child_fq = get_fq(
mass_bins.mass_mid,
child_cq.zsnap,
lit = child_cq.fq_prop['name']
)
# f_Q of descendant
child_cq.predict_nsnap = child_cq.nsnap - predict_step
if child_cq.predict_nsnap < 0:
child_cq.predict_nsnap = 0
descendant_fq = get_fq_nsnap(
mass_bins.mass_mid,
child_cq.predict_nsnap,
lit = child_cq.fq_prop['name']
)
descendant_tcosmic = util.get_t_nsnap(child_cq.predict_nsnap)
# expected number of quiescent galaxies = Ngal * f_Q
# N_gal,exp * f_Q(M_bin)
exp_ngal_q = np.rint( child_fq * ngal_mass_bin ).astype(int)
child_galtype_list = [
child_cq.gal_type[mass_bin_indices[i_m][0]]
for i_m in xrange(mass_bins.nbins)
]
sf_massbin_indices = [
(mass_bin_indices[i_m][0])[np.where(child_galtype_list[i_m] == 'star-forming')]
for i_m in xrange(mass_bins.nbins)
]
ngal_sf_mass_bin = [
len(sf_massbin_indices[i_m])
for i_m in xrange(mass_bins.nbins)
]
# quiescent + quenching galaxies
q_massbin_indices = [
(mass_bin_indices[i_m][0])[np.where(child_galtype_list[i_m] == 'quiescent')]
for i_m in xrange(mass_bins.nbins)
]
ngal_q_obs = [
len(np.where(sfq_classify(
child_cq.mass[q_massbin_index],
child_cq.sfr[q_massbin_index],
child_cq.zsnap) == 'quiescent'
)[0])
for q_massbin_index in q_massbin_indices
]
ngal2quench = exp_ngal_q - ngal_q_obs
print ngal2quench
#ngal2quench = exp_ngal_q - ngal_mass_bin + ngal_sf_mass_bin
exp_exp_ngal_q = np.rint( descendant_fq * ngal_mass_bin ).astype(int)
ngal2quenchfuture = exp_exp_ngal_q - ngal_q_obs
quench_index = []
quenching_start_time = time.time()
# Quench a number of star-forming galaxies in order to match the
# quiescent fraction at the next cosmic time step
# Tau specifier
if child_cq.tau_prop['name'] in ('instant', 'constant', 'satellite', 'long'):
tau_str = ''.join(['_', child_cq.tau_prop['name'], 'tau'])
elif child_cq.tau_prop['name'] in ('line'):
tau_str = ''.join([
'_', child_cq.tau_prop['name'], 'tau',
'_Mfid', str(child_cq.tau_prop['fid_mass']),
'_slope', str(round(child_cq.tau_prop['slope'], 4)),
'_yint', str(round(child_cq.tau_prop['yint'],4))
])
f = open(''.join(['dat/f_quenching/quenching_fraction', tau_str, '_nsnap', str(child_cq.nsnap), '.dat']), 'w')
f.write('# mass, f_quenching\n')
f_quenchings = []
for i_m in xrange(mass_bins.nbins):
quench_is, f_quenching = quenching_galaxies_massbin(
child_cq,
child_cq.t_cosmic - parent_cq.t_cosmic,
descendant_tcosmic - parent_cq.t_cosmic,
child_cq.prev_fquenching[i_m],
mass_bins.mass_mid[i_m],
mass_bin_indices[i_m],
sf_massbin_indices[i_m],
ngal2quench[i_m],
ngal2quenchfuture[i_m],
quiet=quiet
)
print mass_bins.mass_mid[i_m], f_quenching, child_cq.prev_fquenching[i_m]
f_quenchings.append(f_quenching)
quench_index += quench_is
f.write(
'\t'.join([str(mass_bins.mass_mid[i_m]), str(f_quenching), '\n'])
)
f.close()
quench_index = np.array(quench_index)
child_cq.prev_fquenching = f_quenchings
if len(quench_index) != 0:
child_cq.gal_type[quench_index] = 'quiescent' # boom quenched
# assign quenching e-folds for quenching galaxies
child_cq.tau[quench_index] = get_quenching_efold(
child_cq.parent_mass[quench_index],
tau_param = child_cq.tau_prop
)
tau_quench = np.log10( np.exp(
-(child_cq.t_cosmic - parent_cq.t_cosmic) / child_cq.tau[quench_index]
))
child_cq.sfr[quench_index] = child_cq.parent_sfr[quench_index] + tau_quench
child_cq.ssfr[quench_index] = child_cq.sfr[quench_index] - child_cq.mass[quench_index]
q_ssfr_mean = util.get_q_ssfr_mean(child_cq.mass[quench_index])
child_cq.q_ssfr[quench_index] = 0.18 * np.random.randn(len(quench_index)) + q_ssfr_mean
if not quiet:
print 'Quenching takes ', time.time() - quenching_start_time
print "Child's redshift = ", child_cq.zsnap
# deal with orphans ------------------------------------------------------------
if not quiet:
orphan_time = time.time()
print len(child_cq.gal_type[child_cq.gal_type == '']), ' child galaxies are orphans'
child_cq = assign_sfr(child_cq, tau_prop = child_cq.tau_prop, quiet=quiet)
if len(child_cq.gal_type[child_cq.gal_type == '']) != 0:
print len(child_cq.gal_type[child_cq.gal_type == '']), ' child galaxies are orphans'
raise ValueError()
if not quiet:
print 'Orphan care takes ', time.time() - orphan_time
# deal with galaxies that are being quenched beyond the designated final quenching
overquench_time = time.time()
over_quenched = np.where(child_cq.ssfr < child_cq.q_ssfr)
child_cq.ssfr[over_quenched] = child_cq.q_ssfr[over_quenched]
child_cq.tau[over_quenched] = -999.0 # done quenching
if not quiet:
print 'Overquenching takes ', time.time() - overquench_time
print 'Evolution for one time step takes ', time.time() - evo_start
print 'Quiescent Fraction = ', np.float(len(parent_cq.gal_type[parent_cq.gal_type == 'quiescent']))/np.float(len(parent_cq.gal_type))
return child_cq
def test_mass_evol(mass0, t0, tf,
delmass0 = 0.1,
t_initial = 4.0079,
sfrevol_param = [1.0, 1.0],
sfrevol_prop = {'name': 'notperiodic'},
massevol_prop = {'name': 'integrated', 'type': 'euler', 'f_retain': 0.6, 't_step': 0.025},
):
'''
Testing integrated mass evolution
logSFR(z) function is implemented within this module
'''
# spline between z and t_cosmic
z, t = np.loadtxt(Util.snapshottable(), unpack=True, usecols=[2, 3])
z_of_t = interpolate.interp1d(list(reversed(t)), list(reversed(z)), kind='cubic')
z0 = z_of_t(t0)
zf = z_of_t(tf)
# average SFR of SF MS
logsfr_mstar_z, sig_logsfr_mstar_z = get_param_sfr_mstar_z()
def logsfr_notquenched(logmass, t_input, **kwargs):
zi = kwargs['z0']
mass_i = kwargs['mass0']
delmass_i = kwargs['deltamass0']
avglogsfr = logsfr_mstar_z(logmass, zi)
#print 'SFMS(', mass0[0], ',', str(zi), ') ', avglogsfr[0]
# Contribution from the SF evolution of the SFMS
logsfr_sfms = sfr_evol.logsfr_sfms_evol(zi, z_of_t(t_input))
# Contribution from the SF Duty cycle
logsfr_sfduty = sfr_evol.logsfr_sfduty_fluct(
t_initial,
t_input,
delta_sfr=delmass_i,
sfrevol_param=sfrevol_param,
**sfrevol_prop
)
#print logsfr_sfduty
logsfr_f = avglogsfr + logsfr_sfms + logsfr_sfduty
#print np.max(logsfr_f - logsfr_mstar_z(logmass, z_of_t(t_input))), np.min(logsfr_f - logsfr_mstar_z(logmass, z_of_t(t_input)))
return logsfr_f
sfrkwargs = {'z0': z0, 'mass0': mass0, 'deltamass0': delmass0}
mass_f, sfr_f = mass_evol.integrated(
massevol_prop['type'],
logsfr_notquenched,
mass0,
t0,
tf,
f_retain = massevol_prop['f_retain'],
delt = massevol_prop['t_step'],
**sfrkwargs
)
#print sfr_f[:3]
#print logsfr_notquenched(mass_f, tf, **sfrkwargs)[:3]
#print logsfr_mstar_z(mass0, zf)[:3]
return mass_f, sfr_f
def plot_integrated_mass_evol(mass0, t0, tf,
sfrevol_prop = {'name': 'notperiodic'},
massevol_prop = {'name': 'integrated', 'type': 'euler', 'f_retain': 0.6, 't_step': 0.05},
title=None, fig_file=None):
'''
Plot of the integrated mass and SFR evolution as a function of cosmic time
'''
if title is None:
raise ValueError
delt = (tf-t0)/10.0
tfs = np.arange(t0+delt, tf+delt, delt)
z, t = np.loadtxt(Util.snapshottable(), unpack=True, usecols=[2, 3])
z_of_t = interpolate.interp1d(list(reversed(t)), list(reversed(z)), kind='cubic')
# SF MS
logsfr_mstar_z, sig_logsfr_mstar_z = get_param_sfr_mstar_z()
# figure
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(10,12))
# mass evolution
m_sub = fig.add_subplot(211)
# SFR evolution
sub = fig.add_subplot(212)
delta_masses = np.arange(-0.3, 0.3, 0.05)
#delta_masses = [0.0] # test case
for delmass in delta_masses:
# SFR evolution parameters (for SF duty cycle)
sfrevol_param = sfr_evol.get_sfrevol_param(1, np.array([0]), **sfrevol_prop)
masses = [mass0]
sfmssfrs = [logsfr_mstar_z(mass0, z_of_t(t0))]
sfrs = [logsfr_mstar_z(mass0, z_of_t(t0)) + delmass]
mass_i_1 = mass0
t_i_1 = t0
for tf_i in tfs:
mass_i, sfr_i = test_mass_evol(
mass0,
t0,
tf_i,
delmass0 = delmass,
t_initial = t0,
sfrevol_param=sfrevol_param,
sfrevol_prop = sfrevol_prop,
massevol_prop=massevol_prop
)
masses.append(mass_i)
sfrs.append(sfr_i)
sfmssfrs.append(logsfr_mstar_z(mass0, z_of_t(tf_i)))
t_i_1 = tf_i
mass_i_1 = mass_i
tfs = np.hstack([t0, tfs])
for i in xrange(len(masses[0])):
if delmass == delta_masses[0]:
m_label = str(mass0[i])
else:
m_label = None
m_sub.plot(tfs, np.array(masses)[:,i], c=pretty_colors[i], lw=3, alpha=0.5, label=m_label)
for i in xrange(len(masses[0])):
if delmass == delta_masses[0]:
sfr_label = str(mass0[i])
else:
sfr_label = None
sub.plot(tfs, np.array(sfrs)[:,i], c=pretty_colors[i], lw=2, alpha=0.5, label=sfr_label)
sub.plot(tfs, np.array(sfmssfrs)[:,i], c='k', lw=2, ls='--')
for tf_i in tfs:
sub.vlines(tf_i, -1.0, 2.5, color='k', linewidth=1, linestyle=':')
m_sub.set_xlim([t0-2.5, tf+0.5])
m_sub.set_ylim([8.5, 12.5])
m_sub.set_ylabel(r"$\mathtt{log\;M_*}$", fontsize=20)
m_sub.set_title(title)
sub.set_xlim([t0-2.5, tf+0.5])
sub.set_ylim([-2.0, 2.0])
sub.set_xlabel(r"$\mathtt{t_{cosmic}}$", fontsize=20)
sub.set_ylabel(r"$\mathtt{log\;SFR}$", fontsize=20)
sub.legend(loc='upper left')
if fig_file is not None:
figfile = ''.join([
'figure/',
fig_file
])
fig.savefig(figfile, bbox_inches='tight')
#plt.show()
plt.close()
def assign_sfr(
cenque,
sf_prop={'name': 'average'},
fq_prop={'name': 'wetzelsmooth'},
quiet=True,
**kwargs
):
""" Assign star-formation properties to CenQue object.
The function Goes through mass bins and then classifies unassigned
galaxies into quiescent/star-forming based on quiescent fraction
function.
Then SF properties are assigned to the galaxies. For quiescent
galaxies the sSFR is drawn from a log normal distribution about
some predeterimined mu_sSFR. For star-forming galaxies, SFR is
sampled from a designated SF-MS model.
----------------------------------------------------------------
Parameters
----------------------------------------------------------------
cenque : CenQue class object
----------------------------------------------------------------
Notes
----------------------------------------------------------------
* Re imagine how to set up SFR
"""
# time the code
start_time = time.time()
if cenque.mass == None:
raise ValueError()
if cenque.zsnap == None:
raise ValueError()
if 'sf_prop' not in cenque.__dict__.keys():
cenque.sf_prop = sf_prop
else:
if cenque.sf_prop != sf_prop:
cenque.sf_prop = sf_prop
warnings.warn("SF properties do not match; Replacing SF_prop")
if 'fq_prop' not in cenque.__dict__.keys():
cenque.fq_prop = fq_prop
else:
if cenque.fq_prop != fq_prop:
warnings.warn("fQ properties do not match")
mass_bins = cenque.mass_bins
mass_bin_low = mass_bins.mass_low
mass_bin_mid = mass_bins.mass_mid
mass_bin_high = mass_bins.mass_high
# remove galaxies below the minimum and maximum mass and galaxies without children
within_massbin_with_child = np.where(
(cenque.mass > mass_bins.mass_low.min()) &
(cenque.mass <= mass_bins.mass_high.max()) &
(cenque.child >= 0)
)
if (min(cenque.mass) < mass_bins.mass_low.min()) or (max(cenque.mass) > mass_bins.mass_high.max()):
cenque.sample_trim(within_massbin_with_child)
ngal_tot = len(within_massbin_with_child[0])
for attrib in ['sf_prop', 'fq_prop']:
if attrib not in cenque.metadata:
cenque.metadata.append(attrib)
for attrib in ['gal_type', 'sfr', 'ssfr']:
if attrib not in cenque.data_columns:
cenque.data_columns.append(attrib)
if cenque.gal_type is None:
cenque.gal_type = np.array(['' for i in xrange(ngal_tot)], dtype='|S16')
cenque.sfr = np.array([-999. for i in xrange(ngal_tot)])
cenque.ssfr = np.array([-999. for i in xrange(ngal_tot)])
# simplest SFR assignment for starforming galaxies. Use mu_SFR(M*, z)
# and randomly sampled normal delta_SFR.
if sf_prop['name'] == 'average':
sfr_mstar_z, sig_sfr_mstar_z = get_param_sfr_mstar_z()
if 'delta_sfr' not in cenque.__dict__.keys():
cenque.delta_sfr = np.array([-999. for i in xrange(ngal_tot)])
if 'avg_sfr' not in cenque.__dict__.keys():
cenque.avg_sfr = np.array([-999. for i in xrange(ngal_tot)])
extra_attr = ['avg_sfr', 'delta_sfr']
elif sf_prop['name'] == 'average_noscatter':
sfr_mstar_z, sig_sfr_mstar_z = get_param_sfr_mstar_z()
if 'delta_sfr' not in cenque.__dict__.keys():
cenque.delta_sfr = np.array([-999. for i in xrange(ngal_tot)])
if 'avg_sfr' not in cenque.__dict__.keys():
cenque.avg_sfr = np.array([-999. for i in xrange(ngal_tot)])
extra_attr = ['avg_sfr', 'delta_sfr']
else:
raise NotImplementedError()
for attrib in extra_attr:
if attrib not in cenque.data_columns:
cenque.data_columns.append(attrib)
# f_Q(M_*,mid, z_snapshot)
qf_massbin = get_fq(
mass_bin_mid,
cenque.zsnap,
lit = fq_prop['name']
)
massbin_unassigned = [
np.where(
(cenque.mass > mass_bin_low[i_m]) &
(cenque.mass <= mass_bin_high[i_m]) &
(cenque.gal_type == '')
)[0]
for i_m in xrange(mass_bins.nbins)
]
# Ngal(M_mid), Ngal,Q(M_mid) and Ngal,SF(M_mid)
ngal_massbin = np.array([x.size for x in massbin_unassigned])
ngal_q_massbin = np.rint(qf_massbin * ngal_massbin.astype(float)).astype(int)
ngal_sf_massbin = ngal_massbin - ngal_q_massbin
# fail-safe for ngal_q_massbin
if len(np.where(ngal_q_massbin > ngal_massbin)[0]) > 0:
ngal_q_massbin[np.where(ngal_q_massbin > ngal_massbin)] = ngal_massbin[np.where(ngal_q_massbin > ngal_massbin)]
for i_m in xrange(mass_bins.nbins):
if len(massbin_unassigned[i_m]) == 0:
continue
begin_loop_time = time.time()
if not quiet:
print mass_bin_low[i_m], ' < M < ', mass_bin_high[i_m]
print 'fQ = ', qf_massbin[i_m], ' Ngal = ', ngal_massbin[i_m]
print 'Ngal,Q = ', ngal_q_massbin[i_m], ' Ngal,SF = ', ngal_sf_massbin[i_m]
shuffled_massbin_index = np.arange(ngal_massbin[i_m])
np.random.seed()
np.random.shuffle(shuffled_massbin_index)
i_q_end = ngal_q_massbin[i_m]
# Randomly select ngal_q_massbin quiescent galaxies from the
# massbin. Assign them 'quiescent' gal_type and sSFR and SFR
# based on a predetermined gaussian distribution about sSFR.
if ngal_q_massbin[i_m] > 0:
q_massbin = shuffled_massbin_index[:i_q_end]
i_q_massbin = (massbin_unassigned[i_m])[q_massbin]
cenque.gal_type[i_q_massbin] = 'quiescent'
# sample SSFR from log-normal distribution with a preset
# 0.18 dex scatter centered about some predetermined mean
mu_q_ssfr = util.get_q_ssfr_mean(
cenque.mass[i_q_massbin]
)
if len(i_q_massbin) != ngal_q_massbin[i_m]:
print shuffled_massbin_index
print q_massbin
print i_q_massbin
print i_q_end, ngal_q_massbin[i_m]
cenque.ssfr[i_q_massbin] = 0.18 * np.random.randn(ngal_q_massbin[i_m]) + mu_q_ssfr
cenque.sfr[i_q_massbin] = cenque.ssfr[i_q_massbin] + cenque.mass[i_q_massbin]
# ngal_sf_massbin starforming galaxies from the massbin. Assign
# them 'star-forming' gal_type and sSFR and SFR in some manner
if ngal_sf_massbin[i_m] > 0:
#sf_massbin = shuffled_massbin_index[i_q_end:i_sf_end]
sf_massbin = shuffled_massbin_index[i_q_end:]
i_sf_massbin = (massbin_unassigned[i_m])[sf_massbin]
cenque.gal_type[i_sf_massbin] = 'star-forming'
# sample SFR
if sf_prop['name'] == 'average':
mu_sf_sfr = sfr_mstar_z(cenque.mass[i_sf_massbin], cenque.zsnap)
sigma_sf_sfr = sig_sfr_mstar_z(cenque.mass[i_sf_massbin], cenque.zsnap)
cenque.avg_sfr[i_sf_massbin] = mu_sf_sfr
cenque.delta_sfr[i_sf_massbin] = sigma_sf_sfr * np.random.randn(ngal_sf_massbin[i_m])
cenque.sfr[i_sf_massbin] = mu_sf_sfr + cenque.delta_sfr[i_sf_massbin]
if not quiet:
print 'Starforming galaxies: '
print 'Average(SFR) = ', mu_sf_sfr, ' sigma(SFR) = ', sigma_sf_sfr
elif sf_prop['name'] == 'average_noscatter':
mu_sf_sfr = sfr_mstar_z(cenque.mass[i_sf_massbin], cenque.zsnap)
sigma_sf_sfr = 0.0
cenque.avg_sfr[i_sf_massbin] = mu_sf_sfr
cenque.delta_sfr[i_sf_massbin] = sigma_sf_sfr * np.random.randn(ngal_sf_massbin[i_m])
cenque.sfr[i_sf_massbin] = mu_sf_sfr + cenque.delta_sfr[i_sf_massbin]
if not quiet:
print 'Starforming galaxies: '
print 'Average(SFR) = ', mu_sf_sfr, ' sigma(SFR) = ', sigma_sf_sfr
else:
raise NotImplementedError
cenque.ssfr[i_sf_massbin] = cenque.sfr[i_sf_massbin] - cenque.mass[i_sf_massbin]
# double check that SF assign didn't fail anywhere
assign_fail = np.where(
(cenque.sfr == -999.0) |
(cenque.ssfr == -999.0)
)
if len(assign_fail[0]) > 0:
raise NameError('Function failed!')
if not quiet:
print 'Assign SFR function takes', (time.time()-start_time)/60.0, ' minutes'
if 'evol_from' in cenque.cenque_type:
pass
else:
cenque.cenque_type = 'sf_assigned'
return cenque
def qaplot_parameterized_sfms():
""" QAplot comparison for the parameterized SFMS redshift
evolution in comparison to observed SF-MS redshift evolution.
"""
gc_zmid, gc_slope, gc_yint = get_bestfit_sfms_groupcat()
ec_zmid, ec_slope, ec_yint = get_bestfit_sfms_envcount(
fid_mass = 10.5
)
avg_sfr_sfms, sig_sfr_sfms = get_param_sfr_mstar_z()
mass_bin = np.arange(9.0, 12.0, 0.25) # mass bin
fig = plt.figure()
sub = fig.add_subplot(111)
sub.scatter(
0.03,
gc_yint,
c='k',
s=50,
label='Observed GroupCatalog'
)
sub.scatter(
ec_zmid,
ec_yint,
c=prettycolors()[3],
label='Observed EnvCount'
)
sfr_fidmass = np.array([
avg_sfr_sfms(10.5, zmid)
for zmid in np.arange(0.0, 1.0, 0.05)
])
sub.plot(
np.arange(0.0, 1.0, 0.05),
sfr_fidmass,
c='k',
lw=4,
ls='--',
label='Parameterized'
)
sub.plot(
np.arange(0.0, 1.0, 0.05),
sfr_fidmass+0.15,
c=prettycolors()[3],
lw=4,
ls='--'
)
sfr_cutoff = np.array([
sfr_cut(10.5, ec_zmid[i_z])
for i_z in xrange(len(ec_zmid))
])
sub.plot(ec_zmid, sfr_cutoff, c='k', lw=3, ls='--', label='SF/Q Classification')
sub.set_xlim([0.0, 1.0])
sub.set_ylim([-1.0, 2.0])
sub.set_ylabel('SFR(M=10.5,z)', fontsize=20)
sub.set_xlabel('Redshift (z)', fontsize=20)
sub.legend(scatterpoints=1, loc='upper left')
plt.show()