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 BuildGroupCat(Mrcut=18, position='central'):
''' Build Group Catalog
'''
grpcat = GroupCat(Mrcut=Mrcut, position=position)
grpcat.CompileGroupCat()
grpcat.Write()
return None
def GroupCat_iSEDfitMatch(Mrcut=18, position='central'):
''' Test _iSEDfitMatch function of Group Catalog class object
'''
grpcat = GroupCat(Mrcut=Mrcut, position=position)
grpcat.Read()
grpcat._iSEDfitMatch()
return None
def GroupCat_iSEDfitMatch(Mrcut=18, position='central'):
''' Test _iSEDfitMatch function of Group Catalog class object
'''
grpcat = GroupCat(Mrcut=Mrcut, position=position)
grpcat.Read()
grpcat._iSEDfitMatch()
return None
def PlotEvolvedSat(sg_obj):
''' Plot stuff for the evolved satellite population
'''
#infall = np.where(
# (sg_obj.first_infall_mass > 0.) &
# (sg_obj.first_infall_sfr > -999.) &
# (sg_obj.first_infall_t > 5.7))
infall = np.arange(len(sg_obj.first_infall_mass))
# SSFR plot
ssfr_plot = PlotSSFR()
ssfr_plot.plot(mass=sg_obj.mass[infall],
ssfr=sg_obj.ssfr[infall],
line_color=3)
ssfr_plot.GroupCat(position='satellite')
ssfr_plot.set_axes()
ssfr_fig_name = ''.join([
'figure/', 'SSFR.Satellite', '.ABC_posterior', '.', sg_obj.abcrun, '.',
sg_obj.prior_name, '_prior', '.png'
])
ssfr_plot.save_fig(ssfr_fig_name)
plt.close()
# Quiescent fraction plot
fq_plot = PlotFq()
fq_plot.plot(mass=sg_obj.mass[infall],
sfr=sg_obj.sfr[infall],
z=sg_obj.zsnap,
line_color='r',
sfms_prop=sg_obj.sfms_prop,
label='SHAM Sat. Simulation')
grpcat = GroupCat(Mrcut=18, position='satellite')
grpcat.Read()
fq_plot.plot(mass=grpcat.mass,
sfr=grpcat.sfr,
z=np.median(grpcat.z),
line_color='k',
line_style='--',
sfms_prop=sg_obj.sfms_prop,
label='Satellite Group Catalog')
fq_plot.set_axes()
fq_fig_name = ''.join([
'figure/', 'Fq.Satellite', '.ABC_posterior', '.', sg_obj.abcrun, '.',
sg_obj.prior_name, '_prior', '.png'
])
fq_plot.save_fig(fq_fig_name)
plt.close()
return None
def CentralSFR(self):
''' Assign SFRs to central galaxies based on the SDSS DR7 observed central
SFRs.
'''
# central galaxy indices
cen_index = np.where( (self.ilk >= 1.0) & (self.ilk < 2.01) )
self.cen_index = cen_index
cen_mass = getattr(self, 'm.star')[cen_index]
# import central SDSS DR7 galaxies
sdss = GroupCat(Mrcut=18, position='central')
sdss.Read()
dlogm = 0.1
m_arr = np.arange(sdss.mass.min()-0.5*dlogm, 11.2+dlogm, dlogm)
m_low = m_arr[:-1]
m_high = m_arr[1:]
self.sfr = np.repeat(-999., len(getattr(self, 'm.star')))
self.ssfr = np.repeat(-999., len(getattr(self, 'm.star')))
self.position = np.repeat(-1, len(getattr(self, 'm.star')))
self.position[cen_index] = 0
for im in range(len(m_low)):
if im < len(m_low) - 1:
sdss_bin = np.where((sdss.mass >= m_low[im]) & (sdss.mass < m_high[im]))
subh_bin = np.where((cen_mass >= m_low[im]) & (cen_mass < m_high[im]))
else:
sdss_bin = np.where(sdss.mass >= m_low[im])
subh_bin = np.where(cen_mass >= m_low[im])
sdss_sfr = sdss.sfr[sdss_bin]
dist, bin_edges = np.histogram(
sdss_sfr,
range=[-3., 3.],
bins=50,
normed=True)
sfr_cdf = np.zeros(len(bin_edges))
sfr_cdf[1:] = np.cumsum(dist * np.diff(bin_edges))
cdf_interp = interp1d(sfr_cdf, bin_edges, kind='linear')
rand_i = np.random.uniform(size=len(subh_bin[0]))
cen_sfr = cdf_interp(rand_i)
#print cen_sfr.min(), cen_sfr.max()
self.sfr[cen_index[0][subh_bin]] = cen_sfr
self.ssfr[cen_index[0][subh_bin]] = self.sfr[cen_index[0][subh_bin]] - getattr(self, 'm.star')[cen_index[0][subh_bin]]
return None
def ModelSFMS(observable='sdssprimus'):
'''
'''
if observable == 'sdssprimus':
z_bins = [0.1, 0.3, 0.5, 0.7, 0.9]
elif observable == 'groupcat':
z_bins = [0.1]
for i_z, z_mid in enumerate(z_bins):
if observable == 'sdssprimus':
obsdata = PrimusSDSS(z_mid)
obsdata.Read()
z_bin = z_mid
elif observable == 'groupcat':
obsdata = GroupCat(Mrcut=18, position='central')
obsdata.Read()
z_bin = np.mean(obsdata.z)
# SDSS/PRIMUS SFMS of 0 environment galaxies
sfms_plot = PlotSFMS()
sfms_plot.plot(
mass=obsdata.mass,
sfr=obsdata.sfr,
allgal=True,
color='blue')#sfms_plot.pretty_colors[i_z])
# Model SFMS
mstar = np.arange(7.0, 12.01, 0.01)
plt.plot(
mstar,
AverageLogSFR_sfms(mstar, z_bin),
c='k', ls='-',
lw=4, label='Model SFMS')
plt.plot(mstar,
AverageLogSFR_sfms(mstar, z_bin) - ScatterLogSFR_sfms(mstar, z_bin),
ls='--',
lw='3',
color='k')
plt.plot(mstar,
AverageLogSFR_sfms(mstar, z_bin) + ScatterLogSFR_sfms(mstar, z_bin),
ls='--',
lw='3',
color='k')
plt.text(9.25, -4.0, r"$\mathtt{z = "+str(round(z_bin,2))+"}$", fontsize=25)
plt.legend(loc='lower right')
fig_file = ''.join([
'figure/test/',
'SFRevol.ModelSFMS',
'.', observable, '_z', str(round(z_bin, 2)),
'.png'])
sfms_plot.save_fig(fig_file)
plt.close()
def GroupCat(self, Mrcut=18, position='central', **kwargs):
''' Plot sSFR distribution for Group Catalog data
'''
groupcat = GroupCat(Mrcut=Mrcut, position=position)
ssfr_bin_mid, ssfr_hist = groupcat.Ssfr()
# loop through each panel
for i_mass, panel_mass in enumerate(self.panel_mass_bins):
if Mrcut == 18:
z_med = 0.03
elif Mrcut == 19:
z_med = 0.05
elif Mrcut == 20:
z_med = 0.08
ssfr_label= ''.join([
r'SDSS $\mathtt{M_r =', str(Mrcut), '}$'])
if 'lw' in kwargs.keys():
lwid = kwargs['lw']
else:
lwid = 4
if 'ls' in kwargs.keys():
lsty = kwargs['ls']
else:
lsty = '--'
if 'color' in kwargs.keys():
col = kwargs['color']
else:
col = 'k'
self.subs[i_mass].plot(
ssfr_bin_mid[i_mass],
ssfr_hist[i_mass],
color = col,
lw = lwid,
ls = lsty,
label = ssfr_label)
return None
def CenSat_GroupCat():
'''
'''
# import centrals
centrals = GroupCat(Mrcut=18, position='central')
centrals.Read()
# import satellites
satellites = GroupCat(Mrcut=18, position='satellite')
satellites.Read()
N_cen = np.sum(centrals.mass >= 9.7)
N_sat = np.sum(satellites.mass >= 9.7)
print np.float(N_cen) / np.float(N_cen + N_sat)
def GroupCat_SalimMatch_SFR(Mrcut=18, position='central'):
''' Compare the SSFr distribution
'''
grpcat = GroupCat(Mrcut=Mrcut, position=position)
grpcat.Read()
grpcat._Match_OtherSFR(lit='salim2016')
grpcat._Match_OtherSFR(lit='uv')
print len(np.where(grpcat.uv_match != -999)[0])
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(16,16))
fig.subplots_adjust(hspace=0., wspace=0.)
subs = [fig.add_subplot(2, 2, i_mass+1) for i_mass in xrange(4)]
mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]]
for i_mass, mbin in enumerate(mass_bins):
in_massbin = np.where(
(grpcat.mass > mass_bins[i_mass][0]) &
(grpcat.mass <= mass_bins[i_mass][1]) &
(grpcat.salim2016_match != -999) &
(grpcat.uv_match != -999))
vagc_dist, bin_edges = np.histogram(grpcat.sfr[in_massbin], range=[-3., 3.], bins=40, normed=True)
salim_dist, bin_edges = np.histogram(grpcat.salim2016_sfr[in_massbin], range=[-3., 3.], bins=40, normed=True)
uv_dist, bin_edges = np.histogram(grpcat.uv_sfr[in_massbin], range=[-3., 3.], bins=40, normed=True)
subs[i_mass].plot(0.5*(bin_edges[:-1] + bin_edges[1:]), vagc_dist, color='k', lw=4, ls='-')
subs[i_mass].plot(0.5*(bin_edges[:-1] + bin_edges[1:]), salim_dist, color=pretty_colors[3], lw=4, ls='--')
subs[i_mass].plot(0.5*(bin_edges[:-1] + bin_edges[1:]), uv_dist, color=pretty_colors[5], lw=4, ls=':')
#subs[i_mass].plot(salim_bin[i_mass], salim_ssfr[i_mass], color='r', lw=4, ls='--')
subs[i_mass].set_xlim([-3., 3.])
subs[i_mass].set_ylim([0.0, 1.6])
massbin_str = ''.join([
r'$\mathtt{log \; M_{*} = [',
str(mass_bins[i_mass][0]), ',\;',
str(mass_bins[i_mass][1]), ']}$'
])
subs[i_mass].text(-1.5, 1.4, massbin_str,
fontsize=24
)
if i_mass == 0:
subs[i_mass].set_ylabel(r'$\mathtt{P(log \; SFR)}$', fontsize=20)
subs[i_mass].set_xticklabels([])
elif i_mass == 1:
subs[i_mass].set_xticklabels([])
subs[i_mass].set_yticklabels([])
elif i_mass == 2:
#sub.set_yticklabels([])
subs[i_mass].set_ylabel(r'$\mathtt{P(log \; SFR)}$', fontsize=20)
subs[i_mass].set_xlabel(r'$\mathtt{log \; SFR \;[yr^{-1}]}$', fontsize=20)
else:
subs[i_mass].set_yticklabels([])
subs[i_mass].set_xlabel(r'$\mathtt{log \; SFR \;[yr^{-1}]}$', fontsize=20)
fig_file = ''.join([
'figure/test/',
'SFR_comparison',
'.Mr', str(Mrcut),
'.', position,
'.png'])
fig.savefig(fig_file, bbox_inches='tight', dpi=150)
#plt.show()
return None
def ABC(T,
eps_input,
Npart=1000,
cen_tf=None,
cen_prior_name=None,
cen_abcrun=None):
''' ABC-PMC implementation.
Parameters
----------
T : (int)
Number of iterations
eps_input : (float)
Starting epsilon threshold value
N_part : (int)
Number of particles
prior_name : (string)
String that specifies what prior to use.
abcrun : (string)
String that specifies abc run information
'''
abcinh = ABCInherit(cen_tf, abcrun=cen_abcrun, prior_name=cen_prior_name)
# Data (Group Catalog Satellite fQ)
grpcat = GroupCat(Mrcut=18, position='satellite')
grpcat.Read()
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:])
sfq = qfrac.Classify(grpcat.mass,
grpcat.sfr,
np.median(grpcat.z),
sfms_prop=abcinh.sim_kwargs['sfr_prop']['sfms'])
ngal, dum = np.histogram(grpcat.mass, bins=m_bin)
ngal_q, dum = np.histogram(grpcat.mass[sfq == 'quiescent'], bins=m_bin)
data_sum = [M_mid, ngal_q.astype('float') / ngal.astype('float')]
# Simulator
cen_assigned_sat_file = ''.join([
'/data1/hahn/pmc_abc/pickle/', 'satellite', '.cenassign', '.',
cen_abcrun, '_ABC', '.', cen_prior_name, '_prior', '.p'
])
if not os.path.isfile(cen_assigned_sat_file):
sat_cen = AssignCenSFR(cen_tf,
abcrun=cen_abcrun,
prior_name=cen_prior_name)
pickle.dump(sat_cen, open(cen_assigned_sat_file, 'wb'))
else:
sat_cen = pickle.load(open(cen_assigned_sat_file, 'rb'))
def Simz(tt): # Simulator (forward model)
tqdel_dict = {'name': 'explin', 'm': tt[0], 'b': tt[1]}
sat_evol = EvolveSatSFR(sat_cen, tqdelay_dict=tqdel_dict)
sfq_sim = qfrac.Classify(sat_evol.mass,
sat_evol.sfr,
sat_evol.zsnap,
sfms_prop=sat_evol.sfms_prop)
ngal_sim, dum = np.histogram(sat_evol.mass, bins=m_bin)
ngal_q_sim, dum = np.histogram(sat_evol.mass[sfq_sim == 'quiescent'],
bins=m_bin)
sim_sum = [
M_mid,
ngal_q_sim.astype('float') / ngal_sim.astype('float')
]
return sim_sum
# Priors
prior_min = [-11.75, 2.]
prior_max = [-10.25, 4.]
prior = abcpmc.TophatPrior(prior_min, prior_max) # ABCPMC prior object
def rho(simum, datum):
datum_dist = datum[1]
simum_dist = simum[1]
drho = np.sum((datum_dist - simum_dist)**2)
return drho
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.theta_t',
str(pewl), '_', abcrun_flag, '.dat'
])
w_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.w_t',
str(pewl), '_', abcrun_flag, '.dat'
])
dist_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.dist_t',
str(pewl), '_', abcrun_flag, '.dat'
])
eps_file = ''.join([
code_dir(), 'dat/pmc_abc/Satellite.tQdelay.epsilon_', abcrun_flag,
'.dat'
])
eps = abcpmc.ConstEps(T, eps_input)
try:
mpi_pool = mpi_util.MpiPool()
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=rho, # distance function
pool=mpi_pool)
except AttributeError:
abcpmc_sampler = abcpmc.Sampler(
N=Npart, # N_particles
Y=data_sum, # data
postfn=Simz, # simulator
dist=rho) # distance function
abcpmc_sampler.particle_proposal_cls = abcpmc.ParticleProposal
pools = []
f = open(eps_file, "w")
f.close()
eps_str = ''
for pool in abcpmc_sampler.sample(prior, eps, pool=None):
print '----------------------------------------'
print 'eps ', pool.eps
new_eps_str = '\t' + str(pool.eps) + '\n'
if eps_str != new_eps_str: # if eps is different, open fiel and append
f = open(eps_file, "a")
eps_str = new_eps_str
f.write(eps_str)
f.close()
print("T:{0},ratio: {1:>.4f}".format(pool.t, pool.ratio))
print eps(pool.t)
# write theta, weights, and distances to file
np.savetxt(theta_file(pool.t),
pool.thetas,
header='tQdelay_slope, tQdelay_offset')
np.savetxt(w_file(pool.t), pool.ws)
np.savetxt(dist_file(pool.t), pool.dists)
# update epsilon based on median thresholding
eps.eps = np.median(pool.dists)
pools.append(pool)
return pools
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 PlotABC_EvolvedSat(tf, cen_tf=7, cen_abcrun=None, cen_prior_name=None):
''' Plot stuff for the evolved satellite population
'''
# Simulator
cen_assigned_sat_file = ''.join([
'/data1/hahn/pmc_abc/pickle/', 'satellite', '.cenassign', '.',
cen_abcrun, '_ABC', '.', cen_prior_name, '_prior', '.p'
])
if not os.path.isfile(cen_assigned_sat_file):
sat_cen = AssignCenSFR(cen_tf,
abcrun=cen_abcrun,
prior_name=cen_prior_name)
pickle.dump(sat_cen, open(cen_assigned_sat_file, 'wb'))
else:
sat_cen = pickle.load(open(cen_assigned_sat_file, 'rb'))
abcrun_flag = cen_abcrun + '_central'
theta_file = lambda pewl: ''.join([
code_dir(), 'dat/pmc_abc/', 'Satellite.tQdelay.theta_t',
str(pewl), '_', abcrun_flag, '.dat'
])
theta = np.loadtxt(theta_file(tf))
med_theta = [np.median(theta[:, i]) for i in range(len(theta[0]))]
abc_tqdelay_dict = {'name': 'explin', 'm': med_theta[0], 'b': med_theta[1]}
sg_obj = EvolveSatSFR(sat_cen, tqdelay_dict=abc_tqdelay_dict)
#infall = np.arange(len(sg_obj.first_infall_mass))
infall = np.where(
(sg_obj.first_infall_mass > 0.) &
(sg_obj.first_infall_sfr > -999.)) # & (sg_obj.first_infall_t > 5.7))
print len(sg_obj.first_infall_mass)
print len(infall[0])
# SSFR plot
ssfr_plot = PlotSSFR()
ssfr_plot.plot(mass=sg_obj.mass[infall],
ssfr=sg_obj.ssfr[infall],
line_color=3)
ssfr_plot.GroupCat(position='satellite')
ssfr_plot.set_axes()
ssfr_fig_name = ''.join([
'figure/', 'SSFR.Satellite', '.tQdelayABC', '.', sg_obj.abcrun, '.',
sg_obj.prior_name, '_prior', '.png'
])
ssfr_plot.save_fig(ssfr_fig_name)
plt.close()
# Quiescent fraction plot
fq_plot = PlotFq()
fq_plot.plot(mass=sg_obj.mass[infall],
sfr=sg_obj.sfr[infall],
z=sg_obj.zsnap,
line_color='r',
sfms_prop=sg_obj.sfms_prop,
label='SHAM Sat. Simulation')
grpcat = GroupCat(Mrcut=18, position='satellite')
grpcat.Read()
fq_plot.plot(mass=grpcat.mass,
sfr=grpcat.sfr,
z=np.median(grpcat.z),
line_color='k',
line_style='--',
sfms_prop=sg_obj.sfms_prop,
label='Satellite Group Catalog')
fq_plot.set_axes()
fq_fig_name = ''.join([
'figure/', 'Fq.Satellite', '.tQdelayABC', '.', sg_obj.abcrun, '.',
sg_obj.prior_name, '_prior', '.png'
])
fq_plot.save_fig(fq_fig_name)
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 GroupCat_SalimMatch(galprop, lit='uv', Mrcut=18, position='central'):
'''
'''
grpcat = GroupCat(Mrcut=Mrcut, position=position)
grpcat.Read()
grpcat._Match_OtherSFR(lit=lit)
hasmatch = np.where(getattr(grpcat, lit+'_match') >= 0)[0]
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
bkgd = fig.add_subplot(111, frameon=False)
bkgd.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')
fig.subplots_adjust(hspace=0., wspace=0.)
if galprop == 'mass':
subs = [fig.add_subplot(2, 2, i_mass+1) for i_mass in xrange(4)]
mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]]
for i_mass, mbin in enumerate(mass_bins):
mbin = np.where((grpcat.mass[hasmatch] > mass_bins[i_mass][0]) & (grpcat.mass[hasmatch] < mass_bins[i_mass][-1]))
subs[i_mass].scatter(grpcat.mass[hasmatch[mbin]], getattr(grpcat, lit+'_mass')[hasmatch[mbin]], color=pretty_colors[3], s=4)
subs[i_mass].plot([9., 12.], [9., 12.], color='k', lw=4, ls='--')
subs[i_mass].set_xlim([9., 12.])
subs[i_mass].set_ylim([9., 12.])
subs[i_mass].set_xticks([9., 10., 11., 12.])
subs[i_mass].set_yticks([9., 10., 11., 12.])
massbin_str = ''.join([
r'$\mathtt{log \; M^{VAGC}_{*} = [',
str(mass_bins[i_mass][0]), ',\;',
str(mass_bins[i_mass][1]), ']}$'
])
subs[i_mass].text(9.2, 11.6, massbin_str, fontsize=15)
if i_mass == 0:
subs[i_mass].set_xticklabels([])
elif i_mass == 1:
subs[i_mass].set_xticklabels([])
subs[i_mass].set_yticklabels([])
elif i_mass == 3:
subs[i_mass].set_yticklabels([])
bkgd.set_ylabel(lit+' $\mathcal{M}_*$', fontsize=30)
bkgd.set_xlabel('VAGC $\mathcal{M}_*$', fontsize=30)
elif galprop == 'ssfr':
subs = [fig.add_subplot(2, 2, i_mass+1) for i_mass in xrange(4)]
mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]]
for i_mass, mbin in enumerate(mass_bins):
mbin = np.where((grpcat.mass[hasmatch] > mass_bins[i_mass][0]) & (grpcat.mass[hasmatch] < mass_bins[i_mass][-1]))
subs[i_mass].scatter(grpcat.ssfr[hasmatch[mbin]], getattr(grpcat, lit+'_ssfr')[hasmatch[mbin]], color=pretty_colors[3], s=4)
subs[i_mass].plot([-13., -9.], [-13., -9.], color='k', lw=4, ls='--')
subs[i_mass].set_xlim([-13., -9.])
subs[i_mass].set_ylim([-13., -9.])
massbin_str = ''.join([
r'$\mathtt{log \; M^{VAGC}_{*} = [',
str(mass_bins[i_mass][0]), ',\;',
str(mass_bins[i_mass][1]), ']}$'
])
subs[i_mass].text(-12.8, -9.5, massbin_str, fontsize=15)
subs[i_mass].set_xticks([-13, -12, -11, -10, -9])
subs[i_mass].set_yticks([-13, -12, -11, -10, -9])
if i_mass == 0:
subs[i_mass].set_xticklabels([])
elif i_mass == 1:
subs[i_mass].set_xticklabels([])
subs[i_mass].set_yticklabels([])
elif i_mass == 3:
subs[i_mass].set_yticklabels([])
bkgd.set_ylabel(lit+' SSFR', fontsize=30)
bkgd.set_xlabel('VAGC SSFR ', fontsize=30)
elif galprop == 'sfr':
subs = [fig.add_subplot(2, 2, i_mass+1) for i_mass in xrange(4)]
mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]]
for i_mass, mbin in enumerate(mass_bins):
mbin = np.where((grpcat.mass[hasmatch] > mass_bins[i_mass][0]) & (grpcat.mass[hasmatch] < mass_bins[i_mass][-1]))
subs[i_mass].scatter(grpcat.sfr[hasmatch[mbin]], getattr(grpcat, lit+'_sfr')[hasmatch[mbin]], color=pretty_colors[3], s=4)
subs[i_mass].plot([-3., 2.], [-3., 2.], color='k', lw=4, ls='--')
subs[i_mass].set_xlim([-3., 3.])
subs[i_mass].set_ylim([-3., 3.])
massbin_str = ''.join([
r'$\mathtt{log \; M^{VAGC}_{*} = [',
str(mass_bins[i_mass][0]), ',\;',
str(mass_bins[i_mass][1]), ']}$'
])
subs[i_mass].text(-12.8, -9.5, massbin_str, fontsize=15)
subs[i_mass].set_xticks([-3, -2, -1, 0, 1, 2, 3])
subs[i_mass].set_yticks([-3, -2, -1, 0, 1, 2, 3])
if i_mass == 0:
subs[i_mass].set_xticklabels([])
elif i_mass == 1:
subs[i_mass].set_xticklabels([])
subs[i_mass].set_yticklabels([])
elif i_mass == 3:
subs[i_mass].set_yticklabels([])
bkgd.set_ylabel(lit+' SFR', fontsize=30)
bkgd.set_xlabel('VAGC SFR ', fontsize=30)
fig_file = ''.join([
'figure/test/',
'Salim_Comparison',
'.Mr', str(Mrcut),
'.', position,
'.', galprop,
'.', lit,
'.png'])
fig.savefig(fig_file, bbox_inches='tight', dpi=150)
return None
def GroupCat_SalimMatch(galprop, lit='uv', Mrcut=18, position='central'):
'''
'''
grpcat = GroupCat(Mrcut=Mrcut, position=position)
grpcat.Read()
grpcat._Match_OtherSFR(lit=lit)
hasmatch = np.where(getattr(grpcat, lit + '_match') >= 0)[0]
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
bkgd = fig.add_subplot(111, frameon=False)
bkgd.tick_params(labelcolor='none',
top='off',
bottom='off',
left='off',
right='off')
fig.subplots_adjust(hspace=0., wspace=0.)
if galprop == 'mass':
subs = [fig.add_subplot(2, 2, i_mass + 1) for i_mass in xrange(4)]
mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]]
for i_mass, mbin in enumerate(mass_bins):
mbin = np.where((grpcat.mass[hasmatch] > mass_bins[i_mass][0])
& (grpcat.mass[hasmatch] < mass_bins[i_mass][-1]))
subs[i_mass].scatter(grpcat.mass[hasmatch[mbin]],
getattr(grpcat,
lit + '_mass')[hasmatch[mbin]],
color=pretty_colors[3],
s=4)
subs[i_mass].plot([9., 12.], [9., 12.], color='k', lw=4, ls='--')
subs[i_mass].set_xlim([9., 12.])
subs[i_mass].set_ylim([9., 12.])
subs[i_mass].set_xticks([9., 10., 11., 12.])
subs[i_mass].set_yticks([9., 10., 11., 12.])
massbin_str = ''.join([
r'$\mathtt{log \; M^{VAGC}_{*} = [',
str(mass_bins[i_mass][0]), ',\;',
str(mass_bins[i_mass][1]), ']}$'
])
subs[i_mass].text(9.2, 11.6, massbin_str, fontsize=15)
if i_mass == 0:
subs[i_mass].set_xticklabels([])
elif i_mass == 1:
subs[i_mass].set_xticklabels([])
subs[i_mass].set_yticklabels([])
elif i_mass == 3:
subs[i_mass].set_yticklabels([])
bkgd.set_ylabel(lit + ' $\mathcal{M}_*$', fontsize=30)
bkgd.set_xlabel('VAGC $\mathcal{M}_*$', fontsize=30)
elif galprop == 'ssfr':
subs = [fig.add_subplot(2, 2, i_mass + 1) for i_mass in xrange(4)]
mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]]
for i_mass, mbin in enumerate(mass_bins):
mbin = np.where((grpcat.mass[hasmatch] > mass_bins[i_mass][0])
& (grpcat.mass[hasmatch] < mass_bins[i_mass][-1]))
subs[i_mass].scatter(grpcat.ssfr[hasmatch[mbin]],
getattr(grpcat,
lit + '_ssfr')[hasmatch[mbin]],
color=pretty_colors[3],
s=4)
subs[i_mass].plot([-13., -9.], [-13., -9.],
color='k',
lw=4,
ls='--')
subs[i_mass].set_xlim([-13., -9.])
subs[i_mass].set_ylim([-13., -9.])
massbin_str = ''.join([
r'$\mathtt{log \; M^{VAGC}_{*} = [',
str(mass_bins[i_mass][0]), ',\;',
str(mass_bins[i_mass][1]), ']}$'
])
subs[i_mass].text(-12.8, -9.5, massbin_str, fontsize=15)
subs[i_mass].set_xticks([-13, -12, -11, -10, -9])
subs[i_mass].set_yticks([-13, -12, -11, -10, -9])
if i_mass == 0:
subs[i_mass].set_xticklabels([])
elif i_mass == 1:
subs[i_mass].set_xticklabels([])
subs[i_mass].set_yticklabels([])
elif i_mass == 3:
subs[i_mass].set_yticklabels([])
bkgd.set_ylabel(lit + ' SSFR', fontsize=30)
bkgd.set_xlabel('VAGC SSFR ', fontsize=30)
elif galprop == 'sfr':
subs = [fig.add_subplot(2, 2, i_mass + 1) for i_mass in xrange(4)]
mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]]
for i_mass, mbin in enumerate(mass_bins):
mbin = np.where((grpcat.mass[hasmatch] > mass_bins[i_mass][0])
& (grpcat.mass[hasmatch] < mass_bins[i_mass][-1]))
subs[i_mass].scatter(grpcat.sfr[hasmatch[mbin]],
getattr(grpcat, lit + '_sfr')[hasmatch[mbin]],
color=pretty_colors[3],
s=4)
subs[i_mass].plot([-3., 2.], [-3., 2.], color='k', lw=4, ls='--')
subs[i_mass].set_xlim([-3., 3.])
subs[i_mass].set_ylim([-3., 3.])
massbin_str = ''.join([
r'$\mathtt{log \; M^{VAGC}_{*} = [',
str(mass_bins[i_mass][0]), ',\;',
str(mass_bins[i_mass][1]), ']}$'
])
subs[i_mass].text(-12.8, -9.5, massbin_str, fontsize=15)
subs[i_mass].set_xticks([-3, -2, -1, 0, 1, 2, 3])
subs[i_mass].set_yticks([-3, -2, -1, 0, 1, 2, 3])
if i_mass == 0:
subs[i_mass].set_xticklabels([])
elif i_mass == 1:
subs[i_mass].set_xticklabels([])
subs[i_mass].set_yticklabels([])
elif i_mass == 3:
subs[i_mass].set_yticklabels([])
bkgd.set_ylabel(lit + ' SFR', fontsize=30)
bkgd.set_xlabel('VAGC SFR ', fontsize=30)
fig_file = ''.join([
'figure/test/', 'Salim_Comparison', '.Mr',
str(Mrcut), '.', position, '.', galprop, '.', lit, '.png'
])
fig.savefig(fig_file, bbox_inches='tight', dpi=150)
return None
def GroupCat_SalimMatch_SFR(Mrcut=18, position='central'):
''' Compare the SSFr distribution
'''
grpcat = GroupCat(Mrcut=Mrcut, position=position)
grpcat.Read()
grpcat._Match_OtherSFR(lit='salim2016')
grpcat._Match_OtherSFR(lit='uv')
print len(np.where(grpcat.uv_match != -999)[0])
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(16, 16))
fig.subplots_adjust(hspace=0., wspace=0.)
subs = [fig.add_subplot(2, 2, i_mass + 1) for i_mass in xrange(4)]
mass_bins = [[9.7, 10.1], [10.1, 10.5], [10.5, 10.9], [10.9, 11.3]]
for i_mass, mbin in enumerate(mass_bins):
in_massbin = np.where((grpcat.mass > mass_bins[i_mass][0])
& (grpcat.mass <= mass_bins[i_mass][1])
& (grpcat.salim2016_match != -999)
& (grpcat.uv_match != -999))
vagc_dist, bin_edges = np.histogram(grpcat.sfr[in_massbin],
range=[-3., 3.],
bins=40,
normed=True)
salim_dist, bin_edges = np.histogram(grpcat.salim2016_sfr[in_massbin],
range=[-3., 3.],
bins=40,
normed=True)
uv_dist, bin_edges = np.histogram(grpcat.uv_sfr[in_massbin],
range=[-3., 3.],
bins=40,
normed=True)
subs[i_mass].plot(0.5 * (bin_edges[:-1] + bin_edges[1:]),
vagc_dist,
color='k',
lw=4,
ls='-')
subs[i_mass].plot(0.5 * (bin_edges[:-1] + bin_edges[1:]),
salim_dist,
color=pretty_colors[3],
lw=4,
ls='--')
subs[i_mass].plot(0.5 * (bin_edges[:-1] + bin_edges[1:]),
uv_dist,
color=pretty_colors[5],
lw=4,
ls=':')
#subs[i_mass].plot(salim_bin[i_mass], salim_ssfr[i_mass], color='r', lw=4, ls='--')
subs[i_mass].set_xlim([-3., 3.])
subs[i_mass].set_ylim([0.0, 1.6])
massbin_str = ''.join([
r'$\mathtt{log \; M_{*} = [',
str(mass_bins[i_mass][0]), ',\;',
str(mass_bins[i_mass][1]), ']}$'
])
subs[i_mass].text(-1.5, 1.4, massbin_str, fontsize=24)
if i_mass == 0:
subs[i_mass].set_ylabel(r'$\mathtt{P(log \; SFR)}$', fontsize=20)
subs[i_mass].set_xticklabels([])
elif i_mass == 1:
subs[i_mass].set_xticklabels([])
subs[i_mass].set_yticklabels([])
elif i_mass == 2:
#sub.set_yticklabels([])
subs[i_mass].set_ylabel(r'$\mathtt{P(log \; SFR)}$', fontsize=20)
subs[i_mass].set_xlabel(r'$\mathtt{log \; SFR \;[yr^{-1}]}$',
fontsize=20)
else:
subs[i_mass].set_yticklabels([])
subs[i_mass].set_xlabel(r'$\mathtt{log \; SFR \;[yr^{-1}]}$',
fontsize=20)
fig_file = ''.join([
'figure/test/', 'SFR_comparison', '.Mr',
str(Mrcut), '.', position, '.png'
])
fig.savefig(fig_file, bbox_inches='tight', dpi=150)
#plt.show()
return None