def PlotFq_t(lit='wetzelsmooth'):
''' Plot the quiescent fraction evolution as a function of M* and t
'''
#prettyplot()
pretty_colors = prettycolors()
M_arr = np.arange(9.0, 12.5, 0.5)
t_arr = np.arange(5.0, 14.0, 0.2)
fig = plt.figure()
sub = fig.add_subplot(111)
for Mstar in M_arr:
fq_M_t = []
for tt in t_arr:
qf = gp.Fq()
fq_M_t.append(
qf.model(np.array([Mstar]), Util.get_zsnap(tt), lit=lit))
sub.plot(t_arr, fq_M_t, label=r"$M_* = " + str(round(Mstar, 2)) + "$")
sub.set_xlim([t_arr.min(), t_arr.max()])
sub.set_ylim([0., 1.])
#plt.show()
fig_file = ''.join(['figure/test/', 'fq_t.', lit, '.png'])
fig.savefig(fig_file, bbox_inches='tight')
return None
def plotCMS_SMF_MS(cenque='default', evol_dict=None):
'''Plot the stellar mass function of the integrated SFR main sequence population
and compare it to the theoretic stellar mass function of the main sequence
population
'''
# import evolved galaxy population
MSpop = CMS.EvolvedGalPop(cenque=cenque, evol_dict=evol_dict)
print MSpop.File()
MSpop.Read()
MSonly = np.where(MSpop.t_quench == 999.) # remove the quenching galaxies
smf = Obvs.getSMF(MSpop.mass[MSonly], weights=MSpop.weight_down[MSonly])
prettyplot()
pretty_colors = prettycolors()
# analytic SMF
theory_smf = Obvs.getSMF(MSpop.M_sham[MSonly], weights=MSpop.weight_down[MSonly])
fig = plt.figure()
sub = fig.add_subplot(111)
sub.plot(theory_smf[0], theory_smf[1], lw=3, c='k', label='Theory')
sub.plot(smf[0], smf[1], lw=3, c=pretty_colors[3], ls='--', label='MS Only')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([6., 12.0])
sub.set_yscale('log')
sub.set_xlabel(r'Mass $\mathtt{M_*}$', fontsize=25)
sub.set_ylabel(r'Stellar Mass Function $\mathtt{\Phi}$', fontsize=25)
sub.legend(loc='upper right', frameon=False)
fig_file = ''.join([UT.fig_dir(), 'SMF_MS.CMS', MSpop._Spec_str(), '.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def Plot_tRapid():
'''
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1)
sub = fig.add_subplot(111)
m_val = np.array([
6.37119e+9, 1.05173e+10, 1.54219e+10, 2.55939e+10, 3.98025e+10,
6.06088e+10, 9.95227e+10, 1.57374e+11
])
t_rap = np.array([
8.04511e-1, 7.06767e-1, 6.69173e-1, 5.48872e-1, 3.23308e-1, 2.85714e-1,
2.25564e-1, 1.80451e-1
])
sub.scatter(m_val, t_rap, c='k', s=16)
m_arr = np.arange(9.5, 12.0, 0.1)
sub.plot(10**m_arr, tRapid(m_arr), c=pretty_colors[1])
sub.plot(10**m_arr,
sfr_evol.getTauQ(m_arr, tau_prop={'name': 'satellite'}),
c=pretty_colors[3])
# x-axis
sub.set_xscale('log')
sub.set_xlim([5 * 10**9, 2 * 10**11.])
# y-axis
sub.set_ylim([0.0, 4.0])
sub.set_ylabel(r'$\mathtt{t_{Q, rapid}}$', fontsize=25)
plt.show()
plt.close()
return None
def Plot_tRapid():
'''
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1)
sub = fig.add_subplot(111)
m_val = np.array([6.37119e+9, 1.05173e+10, 1.54219e+10, 2.55939e+10, 3.98025e+10, 6.06088e+10, 9.95227e+10, 1.57374e+11])
t_rap = np.array([8.04511e-1, 7.06767e-1, 6.69173e-1, 5.48872e-1, 3.23308e-1, 2.85714e-1, 2.25564e-1, 1.80451e-1])
sub.scatter(m_val, t_rap, c='k', s=16)
m_arr = np.arange(9.5, 12.0, 0.1)
sub.plot(10**m_arr, tRapid(m_arr), c=pretty_colors[1])
sub.plot(10**m_arr, sfr_evol.getTauQ(m_arr, tau_prop={'name':'satellite'}), c=pretty_colors[3])
# x-axis
sub.set_xscale('log')
sub.set_xlim([5*10**9, 2*10**11.])
# y-axis
sub.set_ylim([0.0, 4.0])
sub.set_ylabel(r'$\mathtt{t_{Q, rapid}}$', fontsize=25)
plt.show()
plt.close()
return None
def xi(Mr=20, Nmock=500):
'''
Plot xi(r) of the fake observations
'''
prettyplot()
pretty_colors = prettycolors()
xir, cii = Data.data_xi(Mr=Mr, Nmock=Nmock)
rbin = Data.data_xi_bins(Mr=Mr)
fig = plt.figure(1)
sub = fig.add_subplot(111)
sub.plot(rbin, rbin*xir, c='k', lw=1)
sub.errorbar(rbin, rbin*xir, yerr = rbin*cii**0.5 , fmt="ok", ms=1, capsize=2, alpha=1.)
sub.set_xlim([0.1, 15])
sub.set_ylim([1, 10])
sub.set_yscale("log")
sub.set_xscale("log")
sub.set_xlabel(r'$\mathtt{r}\; (\mathtt{Mpc})$', fontsize=25)
sub.set_ylabel(r'$\mathtt{r} \xi_{\rm gg}$', fontsize=25)
fig_file = ''.join([util.fig_dir(),
'xi.Mr', str(Mr), '.Nmock', str(Nmock), '.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def PlotLee2015_SFMS_zdep():
''' Plot the S0 term (redshift dependence term of the SFMS parmaterization)
of the Lee et al. (2015) SFMS fits.
'''
z_mid = np.array([0.36, 0.55, 0.70, 0.85, 0.99, 1.19])
S0 = np.array([0.80, 0.99, 1.23, 1.35, 1.53, 1.72])
S0_err = np.array([0.019, 0.015, 0.016, 0.014, 0.017, 0.024])
zslope, const = Lee2015_SFMS_zslope()
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
sub.errorbar(z_mid, S0, yerr=S0_err, c='k', elinewidth=3, capsize=10, label='Lee et al. (2015)')
sub.plot(z_mid, zslope * (z_mid - 0.05) + const, c=pretty_colors[1], lw=2, ls='--', label='Best fit')
ztext = '\n'.join(['Redshift slope', r'$\mathtt{A_z='+str(round(zslope,2))+'}$'])
sub.text(0.1, 1.5, ztext, fontsize=20)
sub.legend(loc='lower right')
sub.set_ylabel('Lee et al. (2015) $S_0$ term', fontsize=25)
sub.set_xlim([0.0, 1.5])
sub.set_xlabel('Redshift $(\mathtt{z})$', fontsize=25)
fig_file = ''.join(['figure/', 'Lee2015_SFMS_zdep.png'])
fig.savefig(fig_file, bbox_inches='tight')
return None
def gmf(Mr=20, Nmock=500):
'''
Plot Group Multiplicty Function of fake observations
'''
prettyplot()
pretty_colors = prettycolors()
# import fake obs GMF
gmf, sig_gmf = Data.data_gmf(Mr=Mr, Nmock=Nmock)
# group richness bins
gmf_bin = Data.data_gmf_bins()
fig = plt.figure(1)
sub = fig.add_subplot(111)
sub.errorbar(
0.5*(gmf_bin[:-1]+gmf_bin[1:]), gmf, yerr=sig_gmf,
fmt="ok", capsize=1.0
)
sub.set_xlim([1, 60])
sub.set_yscale('log')
sub.set_ylabel(r"Group Multiplicity Function (h$^{3}$ Mpc$^{-3}$)", fontsize=20)
sub.set_xlabel(r"$\mathtt{Group\;\;Richness}$", fontsize=20)
# save to file
fig_file = ''.join([util.fig_dir(),
'gmf.Mr', str(Mr), '.Nmock', str(Nmock), '.png'])
fig.savefig(fig_file, bbox_inches='tight')
return None
def Test_nsnap_start(nsnap_ancestor=20):
'''
'''
subhist = Cat.PureCentralHistory(nsnap_ancestor=20)
subcat = subhist.Read(downsampled='33')
pretty_colors = prettycolors()
fig = plt.figure(1)
sub = fig.add_subplot(111)
masses = subcat['m.star']
weights = subcat['weights']
for i in range(1, 21)[::-1]:
started = np.where(subcat['nsnap_start'] == i)
mf = Obvs.getMF(masses[started], weights=weights[started])
if i == 20:
mf_list = [np.zeros(len(mf[0]))]
mf_list.append(mf[1] + mf_list[-1])
for i in range(len(mf_list)-1):
sub.fill_between(mf[0], mf_list[i], mf_list[i+1], edgecolor=None, color=pretty_colors[i])
# x-axis
sub.set_xlim([8., 12.])
# y-axis
sub.set_yscale("log")
plt.show()
def PlotFq_WetzelComp():
''' Compare the quiescent function evolution for different analytic prescriptions
of Wetzel et al. (2013)
'''
lit = ['wetzel', 'wetzel_alternate']
M_arr = np.arange(9.0, 12.2, 0.2)
#z_arr = np.arange(0.0, 1.2, 0.2)
z_arr = np.array([0.0, 0.36, 0.66, 0.88])
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
qf = gp.Fq()
for il, l in enumerate(lit):
if il == 0:
ls = '-'
elif il == 1:
ls = '--'
for iz, z in enumerate(z_arr):
if iz == 0:
label = l.title()
else:
label = None
fq_M = qf.model(M_arr, z, lit=l)
sub.plot(M_arr,
fq_M,
c=pretty_colors[iz],
lw=3,
ls=ls,
label=label)
# Now plot Tinker et al. (2013)'s fQ^cen
for iz, z in enumerate(z_arr):
if z != 0.0:
fq_file = ''.join(
['dat/observations/cosmos_fq/', 'stats_z',
str(iz), '.fq_cen'])
mmm, fqcen = np.loadtxt(fq_file, unpack=True, usecols=[0, 1])
mmm = np.log10(mmm)
else:
fq_file = ''.join(
['dat/observations/cosmos_fq/', 'fcen_red_sdss_scatter.dat'])
mmm, fqcen = np.loadtxt(fq_file, unpack=True, usecols=[0, 1])
sub.scatter(mmm, fqcen, c=pretty_colors[iz], lw=0, s=10)
sub.set_xlim([9.0, 12.])
sub.set_xlabel(r'$\mathtt{M_{*}}$', fontsize=25)
sub.set_ylim([0., 1.])
sub.set_ylabel(r'$\mathtt{f_Q}$', fontsize=25)
sub.legend(loc='upper left')
fig_file = ''.join(['figure/test/', 'Fq_Wetzel_Comparison.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def PlotkPk_forcomp(name, version):
'''
'''
peek = PK.Pk(name, version)
peek.Read()
#prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(10,10))
sub = fig.add_subplot(111)
sub.plot(peek.k, peek.k * peek.p2k, lw=3, c=pretty_colors[0])
peek._Rebin_k(np.arange(0.005, 0.41, 0.01))
sub.plot(peek.k, peek.k * peek.p2k, lw=3, c=pretty_colors[3])
# axes
sub.set_xlim([0.0, 0.4])
sub.set_xticks(np.arange(0., 0.45, 0.05))
sub.set_xlabel('$\mathtt{k} (h/\mathtt{Mpc})$', fontsize=30)
sub.set_ylim([0., 700.])
#sub.set_yticks(np.arange(0., 1100., 100.))
sub.set_ylabel('$\mathtt{k P_0(k)}$', fontsize=30)
fig.savefig('comp.png')
plt.close()
return None
def PlotFq_t(lit='wetzelsmooth'):
''' Plot the quiescent fraction evolution as a function of M* and t
'''
#prettyplot()
pretty_colors = prettycolors()
M_arr = np.arange(9.0, 12.5, 0.5)
t_arr = np.arange(5.0, 14.0, 0.2)
fig = plt.figure()
sub = fig.add_subplot(111)
for Mstar in M_arr:
fq_M_t = []
for tt in t_arr:
qf = gp.Fq()
fq_M_t.append(qf.model(np.array([Mstar]), Util.get_zsnap(tt), lit=lit))
sub.plot(t_arr, fq_M_t, label=r"$M_* = "+str(round(Mstar,2))+"$")
sub.set_xlim([t_arr.min(), t_arr.max()])
sub.set_ylim([0., 1.])
#plt.show()
fig_file = ''.join(['figure/test/', 'fq_t.', lit, '.png'])
fig.savefig(fig_file, bbox_inches='tight')
return None
def SubhaloSMF(type, scatter=0.0, source='li-drory-march',
nsnap_ancestor=20 ):
''' Test the Subhalos/CentralSubhalos imported from TreePM by
comparing their measured SMFs to the analytic SMFs
Parameters
----------
type : string
String that specifies whether we're interested in CentralSubhalos
or all Subhalos.
scatter : float
Float that specifies the scatter in the SMHM relation, which
affects the SHAM masses
source : string
String that specifies which SMF is used for SHAM
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
for i_snap in range(1, nsnap_ancestor+1):
smf = SMF()
if type == 'all':
subh = Subhalos()
elif type == 'central':
subh = CentralSubhalos()
else:
raise ValueError
subh.Read(i_snap, scatter=scatter, source=source, nsnap_ancestor=nsnap_ancestor)
subh_mass, subh_phi = smf.Obj(subh, dlogm=0.1, LF=False)
sub.plot(subh_mass, subh_phi, lw=4, c=pretty_colors[i_snap % 19], alpha=0.5)
analytic_mass, analytic_phi = smf.analytic(get_z_nsnap(i_snap), source=source)
sub.plot(analytic_mass, analytic_phi, lw=4, ls='--', c=pretty_colors[i_snap % 19],
label=r"$ z = "+str(round(get_z_nsnap(i_snap),2))+"$")
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([6.0, 12.0])
#sub.legend(loc='upper right')
if type == 'all':
subhalo_str = 'subhalo'
elif type == 'central':
subhalo_str = 'central_subhalo'
else:
raise ValueError
plt.show()
fig_file = ''.join(['figure/test/'
'SubhaloSMF',
'.', subhalo_str,
'.scatter', str(round(scatter,2)),
'.ancestor', str(nsnap_ancestor),
'.', source,
'.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def test_Evolver_logSFRinitiate(sfh, nsnap0=None):
''' Test the log(SFR) initiate step within Evolver.Evolve()
'''
# load in Subhalo Catalog (pure centrals)
subhist = Cat.PureCentralHistory(nsnap_ancestor=nsnap0)
print subhist.File()
subcat = subhist.Read()
# load in generic theta (parameter values)
theta = Evol.defaultTheta(sfh)
for k in theta.keys():
print k, '---', theta[k]
eev = Evol.Evolver(subcat, theta, nsnap0=nsnap0)
eev.Initiate()
eev.Evolve(forTests=True)
#subcat = eev.SH_catalog
sfr_kwargs = eev.sfr_kwargs # SFR keyword arguments
#logSFR_logM_z = self.logSFR_logM_z # logSFR(logM, z) function
prettyplot()
pretty_colors = prettycolors()
if sfh in ['random_step', 'random_step_fluct']:
#print 'dlogSFR_amp', sfr_kwargs['dlogSFR_amp'].shape
#print 'tsteps', sfr_kwargs['tsteps'].shape
i_rand = np.random.choice(range(sfr_kwargs['dlogSFR_amp'].shape[0]), size=10, replace=False)
fig = plt.figure()
sub = fig.add_subplot(111)
for ii in i_rand:
#print sfr_kwargs['tsteps'][ii, :]
#print sfr_kwargs['dlogSFR_amp'][ii, :]
sub.plot(sfr_kwargs['tsteps'][ii, :], sfr_kwargs['dlogSFR_amp'][ii, :])
sub.set_xlim([UT.t_nsnap(nsnap0), UT.t_nsnap(1)])
plt.show()
elif sfh in ['random_step_abias']:
i_rand = np.random.choice(range(sfr_kwargs['dlogSFR_amp'].shape[0]), size=10, replace=False)
fig = plt.figure()
sub = fig.add_subplot(111)
for ii in i_rand: #range(sfr_kwargs['dlogSFR_amp'].shape[1]):
#print sfr_kwargs['tsteps'][ii, :]
#print sfr_kwargs['dlogSFR_amp'][ii, :]
sub.scatter(sfr_kwargs['dMhalos'][ii,:], sfr_kwargs['dlogSFR_corr'][ii,:])
sub.set_xlim([-0.2, 0.2])
sub.set_ylim([-3.*theta['sfh']['sigma_corr'], 3.*theta['sfh']['sigma_corr']])
plt.show()
else:
raise NotImplementedError
return None
def TrueObservables(Mr=21, b_normal=0.25):
''' Plot xi and gmf for the data
'''
# xi data
xi_data = Data.data_xi(Mr=Mr, b_normal=b_normal)
cov_data = Data.data_cov(Mr=Mr, b_normal=b_normal, inference='mcmc')
data_xi_cov = cov_data[1:16, 1:16]
xi_r_bin = Data.data_xi_bin(Mr=Mr)
# gmf data
data_gmf = Data.data_gmf(Mr=Mr, b_normal=b_normal)
cov_data = Data.data_cov(Mr=Mr, b_normal=b_normal, inference='mcmc')
data_gmf_cov = cov_data[16:, 16:]
r_binedge = Data.data_gmf_bins()
gmf_r_bin = 0.5 * (r_binedge[:-1] + r_binedge[1:])
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(12,6))
sub_xi = fig.add_subplot(121)
sub_gmf = fig.add_subplot(122)
#sub_xi.errorbar(xi_r_bin, xi_data, yerr = np.sqrt(np.diag(data_xi_cov)), fmt="o", color='k',
# markersize=0, lw=0, capsize=3, elinewidth=1.5)
#sub_xi.scatter(xi_r_bin, xi_data, c='k', s=10, lw=0)
sub_xi.fill_between(xi_r_bin,
xi_data-np.sqrt(np.diag(data_xi_cov)), xi_data+np.sqrt(np.diag(data_xi_cov)),
color=pretty_colors[1])
sub_xi.set_yscale('log')
sub_xi.set_xscale('log')
sub_xi.set_xlim(0.1, 20)
sub_xi.set_xlabel(r'$\mathtt{r}\; [\mathtt{Mpc}/h]$', fontsize=25)
sub_xi.set_ylabel(r'$\xi(r)$', fontsize=25)
#sub_gmf.errorbar(gmf_r_bin, data_gmf, yerr=np.sqrt(np.diag(data_gmf_cov)),
# fmt="o", color='k',
# markersize=0, lw=0, capsize=4, elinewidth=2)
#sub_gmf.scatter(gmf_r_bin, data_gmf, s=15, lw=0, c='k', label='Mock Observation')
sub_gmf.fill_between(gmf_r_bin,
data_gmf-np.sqrt(np.diag(data_gmf_cov)), data_gmf+np.sqrt(np.diag(data_gmf_cov)),
color=pretty_colors[1])
sub_gmf.set_xlim(1, 20)
sub_gmf.set_xlabel(r'$\mathtt{N}$ (Group Richness)', fontsize=25)
sub_gmf.yaxis.tick_right()
sub_gmf.yaxis.set_ticks_position('both')
sub_gmf.yaxis.set_label_position('right')
sub_gmf.set_ylim([10**-7, 2.0*10**-4])
sub_gmf.set_yscale('log')
sub_gmf.set_ylabel(r'$\zeta(\mathtt{N})$', fontsize=25)
fig.subplots_adjust(hspace=0.05)
fig_name = ''.join([ut.fig_dir(),
'paper.data_observables',
'.pdf'])
fig.savefig(fig_name, bbox_inches='tight', dpi=150)
plt.close()
return None
def TrueObservables(Mr=21, b_normal=0.25):
''' Plot xi and gmf for the data
'''
# xi data
xi_data = Data.data_xi(Mr=Mr, b_normal=b_normal)
cov_data = Data.data_cov(Mr=Mr, b_normal=b_normal, inference='mcmc')
data_xi_cov = cov_data[1:16, 1:16]
xi_r_bin = Data.data_xi_bin(Mr=Mr)
# gmf data
data_gmf = Data.data_gmf(Mr=Mr, b_normal=b_normal)
cov_data = Data.data_cov(Mr=Mr, b_normal=b_normal, inference='mcmc')
data_gmf_cov = cov_data[16:, 16:]
r_binedge = Data.data_gmf_bins()
gmf_r_bin = 0.5 * (r_binedge[:-1] + r_binedge[1:])
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(12, 6))
sub_xi = fig.add_subplot(121)
sub_gmf = fig.add_subplot(122)
#sub_xi.errorbar(xi_r_bin, xi_data, yerr = np.sqrt(np.diag(data_xi_cov)), fmt="o", color='k',
# markersize=0, lw=0, capsize=3, elinewidth=1.5)
#sub_xi.scatter(xi_r_bin, xi_data, c='k', s=10, lw=0)
sub_xi.fill_between(xi_r_bin,
xi_data - np.sqrt(np.diag(data_xi_cov)),
xi_data + np.sqrt(np.diag(data_xi_cov)),
color=pretty_colors[1])
sub_xi.set_yscale('log')
sub_xi.set_xscale('log')
sub_xi.set_xlim(0.1, 20)
sub_xi.set_xlabel(r'$\mathtt{r}\; [\mathtt{Mpc}/h]$', fontsize=25)
sub_xi.set_ylabel(r'$\xi(r)$', fontsize=25)
#sub_gmf.errorbar(gmf_r_bin, data_gmf, yerr=np.sqrt(np.diag(data_gmf_cov)),
# fmt="o", color='k',
# markersize=0, lw=0, capsize=4, elinewidth=2)
#sub_gmf.scatter(gmf_r_bin, data_gmf, s=15, lw=0, c='k', label='Mock Observation')
sub_gmf.fill_between(gmf_r_bin,
data_gmf - np.sqrt(np.diag(data_gmf_cov)),
data_gmf + np.sqrt(np.diag(data_gmf_cov)),
color=pretty_colors[1])
sub_gmf.set_xlim(1, 20)
sub_gmf.set_xlabel(r'$\mathtt{N}$ (Group Richness)', fontsize=25)
sub_gmf.yaxis.tick_right()
sub_gmf.yaxis.set_ticks_position('both')
sub_gmf.yaxis.set_label_position('right')
sub_gmf.set_ylim([10**-7, 2.0 * 10**-4])
sub_gmf.set_yscale('log')
sub_gmf.set_ylabel(r'$\zeta(\mathtt{N})$', fontsize=25)
fig.subplots_adjust(hspace=0.05)
fig_name = ''.join([ut.fig_dir(), 'paper.data_observables', '.pdf'])
fig.savefig(fig_name, bbox_inches='tight', dpi=150)
plt.close()
return None
def LineageFinalDescendantSMF(nsnap_ancestor,
subhalo_prop={
'scatter': 0.0,
'source': 'li-march'
},
sfr_prop={
'fq': {
'name': 'wetzelsmooth'
},
'sfr': {
'name': 'average'
}
}):
''' Plot SMF of final descendant. Also mark the composition of the final SMF from Subhalos
that gain stellar mass at different snapshots in the middle of the simulation.
'''
prettyplot()
pretty_colors = prettycolors()
bloodline = Lineage(nsnap_ancestor=nsnap_ancestor,
subhalo_prop=subhalo_prop)
bloodline.Read([1], sfr_prop=sfr_prop)
final_desc = bloodline.descendant_snapshot1
mf = SMF()
smf_plot = plots.PlotSMF()
smf_plot.cgpop(final_desc, line_color='k')
for isnap in range(2, nsnap_ancestor + 1)[::-1]:
started_here = np.where(final_desc.nsnap_genesis == isnap)
mass, phi = mf._smf(final_desc.mass[started_here])
try:
phi_tot += phi
smf_plot.sub.fill_between(mass,
phi_tot - phi,
phi_tot,
color=pretty_colors[isnap],
label='Nsnap=' + str(isnap))
except UnboundLocalError:
phi_tot = phi
smf_plot.sub.fill_between(mass,
np.zeros(len(phi)),
phi,
color=pretty_colors[isnap],
label='Nsnap=' + str(isnap))
smf_plot.set_axes()
fig_file = ''.join([
'figure/test/', 'LineageFinalDescendantSMF', '.ancestor',
str(nsnap_ancestor),
bloodline._file_spec(subhalo_prop=subhalo_prop, sfr_prop=sfr_prop),
'.png'
])
smf_plot.save_fig(fig_file)
return None
def Subhalo_MhaloMag(scatter=0.0, source='cool_ages', nsnap_ancestor=20):
''' Test the Subhalos/CentralSubhalos imported from TreePM by
comparing their measured SMFs to the analytic SMFs
Parameters
----------
scatter : float
Float that specifies the scatter in the SMHM relation, which
affects the SHAM masses
source : string
String that specifies which SMF is used for SHAM
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
for i_snap in [7, 4, 1]:
mu_mag = []
sig_mag = []
subh = Subhalos()
subh.Read(i_snap,
scatter=scatter,
source=source,
nsnap_ancestor=nsnap_ancestor)
subh.mag_r *= -1.
m_halo = getattr(subh, 'halo.m.max')
for m_low in np.arange(10., 15.5, 0.1):
m_bin = np.where((m_halo > m_low) & (m_halo <= m_low + 0.1))
mu_mag.append(np.mean(subh.mag_r[m_bin]))
sig_mag.append(np.std(subh.mag_r[m_bin]))
#sub.errorbar(np.arange(10.05, 15.55, 0.1), mu_mag, yerr=sig_mag, c=pretty_colors[i_snap])
sub.fill_between(np.arange(10.05, 15.55, 0.1),
np.array(mu_mag) - np.array(sig_mag),
np.array(mu_mag) + np.array(sig_mag),
color=pretty_colors[i_snap],
alpha=0.75,
label=r"$ z = " + str(round(get_z_nsnap(i_snap), 2)) +
"$")
sub.set_xlim([10, 15.5])
sub.set_ylim([-17.8, -23.])
sub.set_ylabel('$\mathtt{M}_\mathtt{halo}^\mathtt{peak}$', fontsize=25)
sub.set_ylabel('Magnitude', fontsize=25)
sub.legend(loc='lower right')
fig_file = ''.join([
'figure/test/'
'Subhalo_Mhalo_Mag', '.scatter',
str(round(scatter, 2)), '.ancestor',
str(nsnap_ancestor), '.', source, '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def Plot_fQcentrals():
''' Plot the quiescent fractions from Tinker et al. (2013)
'''
# mass binnning we impose
m_low = np.array([9.5, 10., 10.5, 11., 11.5])
m_high = np.array([10., 10.5, 11., 11.5, 12.0])
m_mid = 0.5 * (m_low + m_high)
# SDSS
fq_file = ''.join(
['dat/observations/cosmos_fq/', 'fcen_red_sdss_scatter.dat'])
m_sdss, fqcen_sdss, N_sdss = np.loadtxt(fq_file,
unpack=True,
usecols=[0, 1, 2])
fqcen_sdss_rebin = []
for im, m_mid_i in enumerate(m_mid):
sdss_mbin = np.where((m_sdss >= m_low[im]) & (m_sdss < m_high[im]))
fqcen_sdss_rebin.append(
np.sum(fqcen_sdss[sdss_mbin] * N_sdss[sdss_mbin].astype('float')) /
np.sum(N_sdss[sdss_mbin].astype('float')))
fqcen_sdss_rebin = np.array(fqcen_sdss_rebin)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
sub.plot([9.] + list(m_mid), [0.] + list(fqcen_sdss_rebin), c='k')
for iz, z in enumerate([0.36, 0.66, 0.88]):
fq_file = ''.join(
['dat/observations/cosmos_fq/', 'stats_z',
str(iz + 1), '.fq_cen'])
m_cosmos, fqcen_cosmos, fqcen_cosmos_low, fqcen_cosmos_high = np.loadtxt(
fq_file, unpack=True, usecols=[0, 1, 2, 3])
m_cosmos = np.log10(m_cosmos)
fqcen_interp = interpolate.interp1d(m_cosmos, fqcen_cosmos)
fqcen_low_interp = interpolate.interp1d(m_cosmos, fqcen_cosmos_low)
fqcen_high_interp = interpolate.interp1d(m_cosmos, fqcen_cosmos_high)
fqcen_cosmos_rebin = fqcen_interp(m_mid)
fqcen_low_cosmos_rebin = fqcen_low_interp(m_mid)
fqcen_high_cosmos_rebin = fqcen_high_interp(m_mid)
sub.fill_between([9.] + list(m_mid),
[0.] + list(fqcen_low_cosmos_rebin),
[0.] + list(fqcen_high_cosmos_rebin),
color=pretty_colors[2 * iz + 1])
sub.set_xlim([8.75, 12.0])
sub.set_xlabel(r'$\mathtt{M_*}$', fontsize=30)
sub.set_ylim([0., 1.0])
sub.set_ylabel(r'$\mathtt{f_Q^{cen}}$', fontsize=30)
plt.show()
def plot_predictions(Mr, nburnins, nchains, assembly = True, clotter = False):
if (assembly == True):
filename = 'Mr'+str(Mr)+'.hdf5'
else:
filename = 'adhoc_Mr'+str(Mr)+'.hdf5'
sample = h5py.File(filename , "r")["mcmc"]
npars = sample.shape[2]
nwalkers = sample.shape[1]
sample = sample[nchains-nburnins:nchains, : , :]
sample = sample.reshape(sample.shape[0]*sample.shape[1] , sample.shape[2])
print np.percentile(sample , [16,50,84] , axis = 0)
if (clotter==False):
model = MCMC_model(Mr)
model_wp = []
for i in xrange(len(sample) - nwalkers*5 , len(sample)-1):
print i
model_wp.append(model._sum_stat(sample[i] , prior_range = None))
np.savetxt("model_wp_assembly_"+str(Mr)+".dat" , np.array(model_wp))
else:
theta_best = np.median(sample, axis = 0)
print theta_best
model = MCMC_model(Mr)
wp_best = model._sum_stat(theta_best , prior_range = None)
if Mr == 19.0:
cov = np.loadtxt("../../data/wpxicov_dr72_bright0_mr19.0_z0.064_nj400")[:12,:12]
data_wp = np.loadtxt("../../data/wpxi_dr72_bright0_mr19.0_z0.064_nj400")[:,1]
if Mr == 20.0:
cov = np.loadtxt("../../data/wpxicov_dr72_bright0_mr20.0_z0.106_nj400")[:12,:12]
data_wp = np.loadtxt("../../data/wpxi_dr72_bright0_mr20.0_z0.106_nj400")[:,1]
prettyplot()
pretty_colors=prettycolors()
fig = plt.figure(1, figsize=(16,12))
ax = fig.add_subplot(111)
wps = np.loadtxt("model_wp_assembly_"+str(Mr)+".dat")
a, b, c, d, e = np.percentile(wps, [2.5, 16, 50, 84, 97.5], axis=0)
rbin = np.loadtxt(path.join(path.dirname(path.abspath(__file__)),
"../", "bin"))
rbin_center = np.mean(rbin , axis = 1)
ax.fill_between(rbin_center, a, e, color=pretty_colors[3], alpha=0.3, edgecolor="none")
ax.fill_between(rbin_center, b, d, color=pretty_colors[3], alpha=0.5, edgecolor="none")
ax.errorbar(rbin_center, data_wp, np.diag(cov)**.5, markersize=0, lw=0, capsize=3, elinewidth=1.5)
#ax.plot(rbin_center , wp_best , color = "red", lw=1.5)
ax.set_xlabel(r'$r_{p}[\mathtt{Mpc}]$', fontsize=27)
ax.set_ylabel(r'$w_{p}(\mathtt{r_{p}})$', fontsize=27)
ax.set_title(r"posterior prediction of $w_p$ for $\mathrm{M_r}$<-"+str(Mr))
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_xticklabels([])
ax.set_xlim([0.05, 25.])
#ax.set_ylim([0.09, 1000.])
fig.savefig("posterior_prediction"+str(Mr)+".pdf", bbox_inches='tight')
plt.close()
return None
def test_analytical():
'''
Quick analystical test for the integration scheme
'''
def logsfr(logmass, t0, **kwargs):
return np.log10(2.0 * np.sqrt(10.0**logmass))
mass0 = 0.0
t_arr = np.arange(0.0, 14., 1.0)
rk4_masses, euler_masses = [0.0], [0.0]
rk4_sfrs, euler_sfrs = [0.0], [0.0]
for tt in t_arr[1:]:
rk4_mass_f, rk4_sfr_f = mass_evol.integrated_rk4(logsfr,
mass0,
np.min(t_arr),
tt,
f_retain=1.0,
delt=0.01)
euler_mass_f, euler_sfr_f = mass_evol.integrated_euler(logsfr,
mass0,
np.min(t_arr),
tt,
f_retain=1.0,
delt=0.005)
rk4_masses.append(rk4_mass_f)
euler_masses.append(euler_mass_f)
rk4_sfrs.append(rk4_sfr_f)
euler_sfrs.append(euler_sfr_f)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(10, 12))
sub = fig.add_subplot(111)
analytic = np.log10((t_arr * 10**9.)**2)
sub.plot(t_arr, (rk4_masses - analytic) / analytic,
lw=4,
c=pretty_colors[1],
label='RK4')
sub.plot(t_arr, (euler_masses - analytic) / analytic,
lw=4,
ls='--',
c=pretty_colors[3],
label='Euler')
sub.set_xlim([0.0, 14.0])
sub.set_xlabel('fake t', fontsize=25)
sub.set_ylabel('(fake integrated M - fake analytic M)/ fake analytic M',
fontsize=25)
#sub.set_ylim([17.0, 20.0])
sub.legend(loc='lower right')
plt.show()
return None
def plotCMS_SFH_AsBias(cenque='default', evol_dict=None):
''' Plot the star formation history of star forming main sequence galaxies
and their corresponding halo mass acretion history
'''
# import evolved galaxy population
egp = CMS.EvolvedGalPop(cenque=cenque, evol_dict=evol_dict)
egp.Read()
MSonly = np.where(egp.t_quench == 999.) # only keep main-sequence galaxies
MSonly_rand = np.random.choice(MSonly[0], size=100)
z_acc, t_acc = UT.zt_table()
t_cosmic = t_acc[1:16]
dt = 0.1
t_arr = np.arange(t_cosmic.min(), t_cosmic.max()+dt, dt)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub1 = fig.add_subplot(211)
sub2 = fig.add_subplot(212)
dsfrt = np.zeros((len(t_arr), len(MSonly_rand)))
for i_t, tt in enumerate(t_arr):
dsfr = SFR.dSFR_MS(tt, egp.sfh_dict)
for ii, i_gal in enumerate(MSonly_rand):
dsfrt[i_t,ii] = dsfr[i_gal]
i_col = 0
for ii, i_gal in enumerate(MSonly_rand):
tlim = np.where(t_arr > egp.tsnap_genesis[i_gal])
tcoslim = np.where(t_cosmic >= egp.tsnap_genesis[i_gal])
halo_hist = (egp.Mhalo_hist[i_gal])[tcoslim][::-1]
halo_exists = np.where(halo_hist != -999.)
if np.power(10, halo_hist[halo_exists]-egp.halo_mass[i_gal]).min() < 0.7:
if i_col < 5:
sub1.plot(t_arr[tlim], dsfrt[:,ii][tlim], lw=3, c=pretty_colors[i_col])
sub2.plot(t_cosmic[tcoslim][::-1][halo_exists],
np.power(10, halo_hist[halo_exists]-egp.halo_mass[i_gal]),
lw=3, c=pretty_colors[i_col])
i_col += 1
sub1.set_xlim([5.0, 13.5])
sub1.set_xticklabels([])
sub1.set_ylim([-1., 1.])
sub1.set_ylabel(r'$\mathtt{SFR(t) - <SFR_{MS}(t)>}$', fontsize=25)
sub2.set_xlim([5.0, 13.5])
sub2.set_xlabel(r'$\mathtt{t_{cosmic}}$', fontsize=25)
sub2.set_ylim([0.0, 1.1])
sub2.set_ylabel(r'$\mathtt{M_h(t)}/\mathtt{M_h(t_f)}$', fontsize=25)
fig_file = ''.join([UT.fig_dir(), 'SFH_AsBias.CMS', egp._Spec_str(), '.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def plotCMS_SFMS(cenque='default', evol_dict=None):
''' Plot the SFR-M* relation of the SFMS and compare to expectations
'''
# import evolved galaxy population
cms = CMS.CentralMS(cenque=cenque)
cms._Read_CenQue()
MSpop = CMS.EvolvedGalPop(cenque=cenque, evol_dict=evol_dict)
MSpop.Read()
MSonly = np.where(MSpop.t_quench == 999.)
delta_m = 0.5
mbins = np.arange(MSpop.mass[MSonly].min(), MSpop.mass[MSonly].max(), delta_m)
sfr_percent = np.zeros((len(mbins)-1, 4))
for im, mbin in enumerate(mbins[:-1]):
within = np.where(
(MSpop.mass[MSonly] > mbin) &
(MSpop.mass[MSonly] <= mbin + delta_m)
)
#sfr_percent[im,:] = np.percentile(MSpop.sfr[MSonly[0][within]], [2.5, 16, 84, 97.5])
sfr_percent[im,:] = UT.weighted_quantile(MSpop.sfr[MSonly[0][within]],
[0.025, 0.16, 0.84, 0.975], weights=MSpop.weight_down[MSonly[0][within]])
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
# parameterized observed SFMS
obvs_mu_sfr = SFR.AverageLogSFR_sfms(0.5*(mbins[:-1]+mbins[1:]), 0.05, sfms_dict=cms.sfms_dict)
obvs_sig_sfr = SFR.ScatterLogSFR_sfms(0.5*(mbins[:-1]+mbins[1:]), 0.05, sfms_dict=cms.sfms_dict)
sub.fill_between(0.5*(mbins[:-1] + mbins[1:]),
obvs_mu_sfr - 2.*obvs_sig_sfr, obvs_mu_sfr + 2.*obvs_sig_sfr,
color=pretty_colors[1], alpha=0.3, edgecolor=None, lw=0)
sub.fill_between(0.5*(mbins[:-1] + mbins[1:]),
obvs_mu_sfr - obvs_sig_sfr, obvs_mu_sfr + obvs_sig_sfr,
color=pretty_colors[1], alpha=0.5, edgecolor=None, lw=0, label='Observations')
# simulation
sub.fill_between(0.5*(mbins[:-1] + mbins[1:]), sfr_percent[:,0], sfr_percent[:,-1],
color=pretty_colors[3], alpha=0.3, edgecolor=None, lw=0)
sub.fill_between(0.5*(mbins[:-1] + mbins[1:]), sfr_percent[:,1], sfr_percent[:,-2],
color=pretty_colors[3], alpha=0.5, edgecolor=None, lw=0, label='Simulated')
# x,y axis
sub.set_xlim([9.0, 12.0])
sub.set_xlabel(r'$\mathtt{M_*}\; [\mathtt{M}_\odot]$', fontsize=25)
sub.set_ylim([-2., 1.])
sub.set_ylabel(r'$\mathtt{SFR}\; [\mathtt{M}_\odot/\mathtt{yr}]$', fontsize=25)
sub.legend(loc='lower right', numpoints=1, markerscale=1.)
fig_file = ''.join([UT.fig_dir(), 'SFMS.CMS', MSpop._Spec_str(), '.png'])
plt.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def test_ancestors_without_descendant(nsnap_ancestor = 20, scatter = 0.0, source='li-drory-march'):
'''
What happens to ancestors that do not have descendants at nsnap = 1
Notes
-----
* More than 50% of the subhalos at nsnap=20 do not have descendants at nsnap = 1.
What happens to them?
* Do they become satellites? --> Some become satellites, others
with no mass subhalos don't stay in the catalog at all
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
# Central subhalo at nsnap_ancestor (ancesotr)
anc = CentralSubhalos()
anc.read(nsnap_ancestor, scatter=scatter, source=source)
# Centrals subhalo at nsnap = 1 (descendant)
des = CentralSubhalos()
des.read(1, scatter=scatter, source=source)
no_descendants = np.invert(np.in1d(anc.index, des.ancestor20))
massive_nodescendants = np.where(anc.mass[no_descendants] > 0.0)
print 'N_SH no descendants ', len(anc.mass[no_descendants])
print 'N_SH total ', len(anc.mass)
print np.float(len(anc.mass[no_descendants]))/np.float(len(anc.mass))
no_des_index = anc.index[no_descendants][massive_nodescendants][:5]
print anc.mass[no_descendants][massive_nodescendants][:5]
for isnap in range(1, nsnap_ancestor)[::-1]:
i_des = CentralSubhalos()
i_des.read(isnap, scatter=scatter, source=source)
#print isnap, np.in1d(no_des_index, i_des.ancestor20)
#if not np.in1d(no_des_index, i_des.ancestor20)[0]:
des_sh = Subhalos()
des_sh.read(isnap, scatter=scatter, source=source)
#if np.sum(np.in1d(des_sh.ancestor20, no_des_index)) != len(no_des_index):
# raise ValueError
print des_sh.ilk[np.in1d(des_sh.ancestor20, no_des_index)][np.argsort(des_sh.ancestor20[np.in1d(des_sh.ancestor20, no_des_index)])]
print 'M* ', des_sh.mass[np.in1d(des_sh.ancestor20, no_des_index)][np.argsort(des_sh.ancestor20[np.in1d(des_sh.ancestor20, no_des_index)])]
print 'M_halo ', getattr(des_sh, 'halo.m')[np.in1d(des_sh.ancestor20, no_des_index)][np.argsort(des_sh.ancestor20[np.in1d(des_sh.ancestor20, no_des_index)])]
#print des_sh.mass[np.in1d(des_sh.index, no_des_index)]
#np.in1d(i_des.ancestor20, no_des_index)
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([6.0, 12.0])
sub.legend(loc='upper right')
def plot_nseriesbox_p_k_mu(imock, plot='2D', Ngrid=3600):
''' Plot P(k,mu). Option for plotting the monopole is included.
'''
# read k, mu, counts, P(k,mu)
k_bin, mu_bin, co, avgP = nseriesbox_pkmu(imock, Ngrid=Ngrid)
prettyplot()
pretty_color = prettycolors()
fig = plt.figure(figsize=(14,10))
sub = fig.add_subplot(111)
if plot == '2D':
colormap = plt.cm.afmhot
cont = sub.pcolormesh(np.arange(-1.0, 1.1, 0.1), k_bin, avgP, cmap=colormap)
plt.colorbar(cont)
# y-axis
sub.set_ylabel(r'$\mathtt{k \;(\mathrm{Mpc}/h})$', fontsize=20)
# x-axis
sub.set_xlabel(r'$\mu$', fontsize=20)
elif plot == 'pk':
proj_p0k = []
proj_p2k = []
Leg2 = 2.5 * (3.0 * mu_bin**2 - 1.0) # L_2(mu)
for i in range(N_kbin):
pkmu_k = avgP[i]
co_k = co[i]
tot_p0k = np.sum(co_k[~np.isnan(pkmu_k)] * pkmu_k[~np.isnan(pkmu_k)])
tot_count = np.sum(co_k[~np.isnan(pkmu_k)])
proj_p0k.append(tot_p0k/tot_count)
tot_p2k = np.sum(co_k[~np.isnan(pkmu_k)] * pkmu_k[~np.isnan(pkmu_k)] * Leg2[~np.isnan(pkmu_k)])
tot_count = np.sum(co_k[~np.isnan(pkmu_k)])
proj_p2k.append(tot_p2k/tot_count)
sub.plot(k_bin, proj_p0k, lw=4, c=pretty_color[0], label='Monopole')
sub.plot(k_bin, proj_p2k, lw=4, c=pretty_color[2], label='Quadrupole')
k, p0k, p2k = np.loadtxt('/home/users/hahn/power360z_BoxN7.dat', unpack=True, usecols=[0,-1,2])
sub.plot(k, p0k * (2*np.pi)**3, ls='--', c='k')
sub.plot(k, p2k * (2*np.pi)**3, ls='--', c='k')
# x-axis
sub.set_xlabel(r'$\mathtt{k \;(\mathrm{Mpc}/h})$', fontsize=25)
sub.set_xlim([10**-3, 10**1])
sub.set_xscale("log")
#y-axis
sub.set_ylabel(r"$\mathtt{P_{l}(k)}$", fontsize=25)
sub.legend(loc='upper right')
else:
raise ValueError
sub.set_yscale("log")
fig.savefig(''.join(['figure/',
'pkmu_', str(Ngrid), 'z_BoxN', str(imock), '.', plot, '.png']),
bbox_inches='tight')
plt.close()
def Plot_dFqdt():
''' Plot the evolution of the derivative of the quiescent fraction as a function of M* and t
for different parameterized models of f_Q
d f_Q / d t
'''
prettyplot()
pretty_colors = prettycolors()
dt = 0.01
M_arr = np.arange(9.0, 12.5, 0.5)
t_arr = np.arange(6.0, 13.0, 0.2)
fig = plt.figure()
sub = fig.add_subplot(111)
for iM, Mstar in enumerate(M_arr):
qf = gp.Fq()
lit = 'wetzelsmooth'
sub.plot(t_arr, [gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_arr],
ls='-',
lw=3,
c=pretty_colors[iM],
label=r"$\mathtt{M}_* = " + str(round(Mstar, 2)) + "$")
lit = 'wetzel'
sub.plot(t_arr, [gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_arr],
ls=':',
lw=3,
c=pretty_colors[iM])
lit = 'cosmosinterp'
t_blah = np.arange(8.0, 9.6, 0.1)
sub.plot(t_blah,
[gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_blah],
ls='--',
lw=3,
c=pretty_colors[iM])
lit = 'cosmosfit'
t_blah = np.arange(8.0, 9.6, 0.1)
sub.plot(t_blah,
[gp.dFqdt(Mstar, tt, lit=lit, dt=dt) for tt in t_blah],
ls='-.',
lw=3,
c=pretty_colors[iM])
sub.set_xlim([t_arr.min(), t_arr.max()])
sub.legend(loc='upper left')
sub.set_ylim([0., 0.2])
#plt.show()
fig_file = ''.join(['figure/test/', 'dfqdt.png'])
fig.savefig(fig_file, bbox_inches='tight')
return None
def PlotFq_WetzelTinker_comparison():
''' Compare the quiescent function evolution for different analytic prescriptions
'''
lit = ['wetzel', 'wetzel_alternate', 'cosmos_tinker']
prettyplot()
pretty_colors = prettycolors()
M_arr = np.arange(9.0, 12.2, 0.2)
z_arr = np.arange(0.0, 1.2, 0.2)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(20,5))
qf = gp.Fq()
for il, l in enumerate(lit):
sub = fig.add_subplot(1,4,il+1)
for iz, z in enumerate(z_arr):
if iz == 0:
label = (l.replace('_', ' ')).title()
else:
label = None
fq_M = qf.model(M_arr, z, lit=l)
sub.plot(M_arr, fq_M, c=pretty_colors[iz], lw=3, label=label)
sub.set_xlim([9.0, 12.])
sub.set_xlabel(r'$\mathtt{M_{*}}$', fontsize=25)
sub.set_ylim([0., 1.])
if il == 0:
sub.set_ylabel(r'$\mathtt{f_Q}$', fontsize=25)
else:
sub.set_yticklabels([])
sub.legend(loc='upper left')
fig_file = ''.join(['figure/test/',
'Fq_Comparison.', '_'.join(lit), '.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def PlotFq_WetzelTinker_comparison():
''' Compare the quiescent function evolution for different analytic prescriptions
'''
lit = ['wetzel', 'wetzel_alternate', 'cosmos_tinker']
prettyplot()
pretty_colors = prettycolors()
M_arr = np.arange(9.0, 12.2, 0.2)
z_arr = np.arange(0.0, 1.2, 0.2)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(20, 5))
qf = gp.Fq()
for il, l in enumerate(lit):
sub = fig.add_subplot(1, 4, il + 1)
for iz, z in enumerate(z_arr):
if iz == 0:
label = (l.replace('_', ' ')).title()
else:
label = None
fq_M = qf.model(M_arr, z, lit=l)
sub.plot(M_arr, fq_M, c=pretty_colors[iz], lw=3, label=label)
sub.set_xlim([9.0, 12.])
sub.set_xlabel(r'$\mathtt{M_{*}}$', fontsize=25)
sub.set_ylim([0., 1.])
if il == 0:
sub.set_ylabel(r'$\mathtt{f_Q}$', fontsize=25)
else:
sub.set_yticklabels([])
sub.legend(loc='upper left')
fig_file = ''.join(
['figure/test/', 'Fq_Comparison.', '_'.join(lit), '.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def SubhaloLF(scatter=0.0, source='cool_ages', nsnap_ancestor=20):
''' Test the Subhalos/CentralSubhalos imported from TreePM by
comparing their measured SMFs to the analytic SMFs
Parameters
----------
scatter : float
Float that specifies the scatter in the SMHM relation, which
affects the SHAM masses
source : string
String that specifies which SMF is used for SHAM
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
for i_snap in [11, 7, 4, 1]:
smf = SMF()
subh = Subhalos()
subh.Read(i_snap,
scatter=scatter,
source=source,
nsnap_ancestor=nsnap_ancestor)
subh.mag_r *= -1.
mass, phi = smf.Obj(subh,
dlogm=0.1,
m_arr=np.arange(-24.0, -16., 0.1),
LF=True)
sub.plot(mass, phi, lw=2, ls='-', c=pretty_colors[i_snap % 19])
analytic_mass, analytic_phi = smf.analytic(get_z_nsnap(i_snap),
source=source)
sub.plot(analytic_mass,
analytic_phi,
lw=4,
ls='--',
c=pretty_colors[i_snap % 19],
label=r"$ z = " + str(round(get_z_nsnap(i_snap), 2)) + "$")
sub.set_yscale('log')
sub.set_ylim([10**-7, 10**-1])
sub.set_xlim([-24.5, -17.8])
sub.legend(loc='upper right')
fig_file = ''.join([
'figure/test/'
'SubhaloLF', '.scatter',
str(round(scatter, 2)), '.ancestor',
str(nsnap_ancestor), '.', source, '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def __init__(self, **kwargs):
'''
Class that generally encompasses all of the plotting I will do for the CenQue project
'''
self.kwargs = kwargs
prettyplot()
self.pretty_colors = prettycolors()
self.fig = plt.figure(figsize=[10, 10])
self.sub = self.fig.add_subplot(1,1,1)
def Plot_fQcentrals():
''' Plot the quiescent fractions from Tinker et al. (2013)
'''
# 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)
sub.plot([9.]+list(m_mid), [0.]+list(fqcen_sdss_rebin), c='k')
for iz, z in enumerate([0.36, 0.66, 0.88]):
fq_file = ''.join(['dat/observations/cosmos_fq/',
'stats_z', str(iz+1), '.fq_cen'])
m_cosmos, fqcen_cosmos, fqcen_cosmos_low, fqcen_cosmos_high = np.loadtxt(fq_file, unpack=True, usecols=[0,1,2,3])
m_cosmos = np.log10(m_cosmos)
fqcen_interp = interpolate.interp1d(m_cosmos, fqcen_cosmos)
fqcen_low_interp = interpolate.interp1d(m_cosmos, fqcen_cosmos_low)
fqcen_high_interp = interpolate.interp1d(m_cosmos, fqcen_cosmos_high)
fqcen_cosmos_rebin = fqcen_interp(m_mid)
fqcen_low_cosmos_rebin = fqcen_low_interp(m_mid)
fqcen_high_cosmos_rebin = fqcen_high_interp(m_mid)
sub.fill_between([9.]+list(m_mid),
[0.]+list(fqcen_low_cosmos_rebin), [0.]+list(fqcen_high_cosmos_rebin),
color=pretty_colors[2*iz+1])
sub.set_xlim([8.75, 12.0])
sub.set_xlabel(r'$\mathtt{M_*}$', fontsize=30)
sub.set_ylim([0., 1.0])
sub.set_ylabel(r'$\mathtt{f_Q^{cen}}$', fontsize=30)
plt.show()
def test_analytical():
'''
Quick analystical test for the integration scheme
'''
def logsfr(logmass, t0, **kwargs):
return np.log10(2.0 * np.sqrt(10.0**logmass))
mass0 = 0.0
t_arr = np.arange(0.0, 14., 1.0)
rk4_masses, euler_masses = [0.0], [0.0]
rk4_sfrs, euler_sfrs = [0.0], [0.0]
for tt in t_arr[1:]:
rk4_mass_f, rk4_sfr_f = mass_evol.integrated_rk4(
logsfr,
mass0,
np.min(t_arr),
tt,
f_retain = 1.0,
delt = 0.01
)
euler_mass_f, euler_sfr_f = mass_evol.integrated_euler(
logsfr,
mass0,
np.min(t_arr),
tt,
f_retain = 1.0,
delt = 0.005
)
rk4_masses.append(rk4_mass_f)
euler_masses.append(euler_mass_f)
rk4_sfrs.append(rk4_sfr_f)
euler_sfrs.append(euler_sfr_f)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(10,12))
sub = fig.add_subplot(111)
analytic = np.log10((t_arr * 10**9.)**2)
sub.plot(t_arr, (rk4_masses-analytic)/analytic, lw=4, c=pretty_colors[1], label='RK4')
sub.plot(t_arr, (euler_masses-analytic)/analytic, lw=4, ls='--', c=pretty_colors[3], label='Euler')
sub.set_xlim([0.0, 14.0])
sub.set_xlabel('fake t', fontsize=25)
sub.set_ylabel('(fake integrated M - fake analytic M)/ fake analytic M', fontsize=25)
#sub.set_ylim([17.0, 20.0])
sub.legend(loc='lower right')
plt.show()
return None
def Subhalo_MhaloMag(scatter=0.0, source='cool_ages',
nsnap_ancestor=20 ):
''' Test the Subhalos/CentralSubhalos imported from TreePM by
comparing their measured SMFs to the analytic SMFs
Parameters
----------
scatter : float
Float that specifies the scatter in the SMHM relation, which
affects the SHAM masses
source : string
String that specifies which SMF is used for SHAM
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
for i_snap in [7, 4, 1]:
mu_mag = []
sig_mag = []
subh = Subhalos()
subh.Read(i_snap, scatter=scatter, source=source, nsnap_ancestor=nsnap_ancestor)
subh.mag_r *= -1.
m_halo = getattr(subh, 'halo.m.max')
for m_low in np.arange(10., 15.5, 0.1):
m_bin = np.where((m_halo > m_low) & (m_halo <= m_low+0.1))
mu_mag.append(np.mean(subh.mag_r[m_bin]))
sig_mag.append(np.std(subh.mag_r[m_bin]))
#sub.errorbar(np.arange(10.05, 15.55, 0.1), mu_mag, yerr=sig_mag, c=pretty_colors[i_snap])
sub.fill_between(np.arange(10.05, 15.55, 0.1),
np.array(mu_mag) - np.array(sig_mag),
np.array(mu_mag) + np.array(sig_mag),
color=pretty_colors[i_snap], alpha=0.75,
label=r"$ z = "+str(round(get_z_nsnap(i_snap),2))+"$")
sub.set_xlim([10, 15.5])
sub.set_ylim([-17.8, -23.])
sub.set_ylabel('$\mathtt{M}_\mathtt{halo}^\mathtt{peak}$', fontsize=25)
sub.set_ylabel('Magnitude', fontsize=25)
sub.legend(loc='lower right')
fig_file = ''.join(['figure/test/'
'Subhalo_Mhalo_Mag',
'.scatter', str(round(scatter,2)),
'.ancestor', str(nsnap_ancestor),
'.', source,
'.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def PlotFq_WetzelComp():
''' Compare the quiescent function evolution for different analytic prescriptions
of Wetzel et al. (2013)
'''
lit = ['wetzel', 'wetzel_alternate']
M_arr = np.arange(9.0, 12.2, 0.2)
#z_arr = np.arange(0.0, 1.2, 0.2)
z_arr = np.array([0.0, 0.36, 0.66, 0.88])
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
qf = gp.Fq()
for il, l in enumerate(lit):
if il == 0:
ls = '-'
elif il == 1:
ls = '--'
for iz, z in enumerate(z_arr):
if iz == 0:
label = l.title()
else:
label = None
fq_M = qf.model(M_arr, z, lit=l)
sub.plot(M_arr, fq_M, c=pretty_colors[iz], lw=3, ls=ls, label=label)
# Now plot Tinker et al. (2013)'s fQ^cen
for iz, z in enumerate(z_arr):
if z != 0.0:
fq_file = ''.join(['dat/observations/cosmos_fq/',
'stats_z', str(iz), '.fq_cen'])
mmm, fqcen = np.loadtxt(fq_file, unpack=True, usecols=[0, 1])
mmm = np.log10(mmm)
else:
fq_file = ''.join(['dat/observations/cosmos_fq/',
'fcen_red_sdss_scatter.dat'])
mmm, fqcen = np.loadtxt(fq_file, unpack=True, usecols=[0, 1])
sub.scatter(mmm, fqcen, c=pretty_colors[iz], lw=0, s=10)
sub.set_xlim([9.0, 12.])
sub.set_xlabel(r'$\mathtt{M_{*}}$', fontsize=25)
sub.set_ylim([0., 1.])
sub.set_ylabel(r'$\mathtt{f_Q}$', fontsize=25)
sub.legend(loc='upper left')
fig_file = ''.join(['figure/test/', 'Fq_Wetzel_Comparison.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def plotCMS_SMHMR_MS(cenque='default', evol_dict=None):
''' Plot the Stellar Mass to Halo Mass Relation of the evolved Gal population
'''
# import evolved galaxy population
egp = CMS.EvolvedGalPop(cenque=cenque, evol_dict=evol_dict)
egp.Read()
# Calculate the SMHMR for the EvolvedGalPop
m_halo_bin = np.arange(10., 15.25, 0.25) # M_halo bins
m_halo_mid = 0.5 * (m_halo_bin[:-1] + m_halo_bin[1:]) # M_halo bin mid
smhmr = np.zeros((len(m_halo_bin)-1, 5))
for im, m_mid in enumerate(m_halo_mid):
inbin = np.where(
(egp.halo_mass > m_halo_bin[im]) &
(egp.halo_mass <= m_halo_bin[im+1]))
smhmr[im,:] = np.percentile(egp.mass[inbin], [2.5, 16, 50, 84, 97.5])
#smhmr[im,:] = UT.weighted_quantile(egp.mass[inbin], [0.025, 0.16, 0.50, 0.84, 0.975],
# weights=egp.weight_down[inbin])
# "observations" with observed scatter of 0.2 dex
obvs_smhmr = np.zeros((len(m_halo_bin)-1, 2))
obvs_smhmr[:,0] = smhmr[:,2] - 0.2
obvs_smhmr[:,1] = smhmr[:,2] + 0.2
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
sub.fill_between(m_halo_mid, smhmr[:,0], smhmr[:,-1],
color=pretty_colors[1], alpha=0.25, edgecolor=None, lw=0)
sub.fill_between(m_halo_mid, smhmr[:,1], smhmr[:,-2],
color=pretty_colors[1], alpha=0.5, edgecolor=None, lw=0,
label=r'$\mathtt{Simulated}$')
sub.plot(m_halo_mid, obvs_smhmr[:,0],
color='k', ls='--', lw=3)
sub.plot(m_halo_mid, obvs_smhmr[:,1],
color='k', ls='--', lw=3,
label=r"$\sigma = \mathtt{0.2}$")
sub.set_xlim([10., 15.0])
sub.set_ylim([9., 12.0])
sub.set_xlabel(r'$\mathtt{log}\;\mathtt{M_{halo}}\;\;[\mathtt{M_\odot}]$', fontsize=25)
sub.set_ylabel(r'$\mathtt{log}\;\mathtt{M_{*}}\;\;[\mathtt{M_\odot}]$', fontsize=25)
sub.legend(loc='upper right')
fig_file = ''.join([UT.fig_dir(), 'SMHMR_MS.CMS', egp._Spec_str(), '.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def fQ_fSFMS(logMfid=10.5):
''' Figure comparing the quiescent fraction based on "traditional" SFMS cut
to the SFMS fraction.
'''
# Read in Jeremy's group catalog with Mr_cut = -18
gc = Cat.Observations('group_catalog', Mrcut=18, position='central')
gc_cat = gc.Read()
# fit the SFMS using lettalkaboutquench sfms fitting
fSFMS = fstarforms()
fit_logm, fit_logsfr = fSFMS.fit(gc_cat['mass'], gc_cat['sfr'], method='gaussmix')
_, fit_fsfms = fSFMS.frac_SFMS()
# now fit a fSFMS(M*)
coeff = np.polyfit(fit_logm-logMfid, fit_fsfms, 1)
# output f_SFMS to data (for posterity)
f = open(''.join([UT.tex_dir(), 'dat/fsfms.dat']), 'w')
f.write('### header ### \n')
f.write('star-formation main sequence (SFMS) fraction: fraction of galaxies \n')
f.write('within a log-normal fit of the SFMS. See paper for details.\n')
f.write('best-fit f_SFMS = '+str(round(coeff[0], 3))+' (log M* - '+str(logMfid)+') + '+str(round(coeff[1],3))+'\n')
f.write('columns: log M*, f_SFMS\n')
f.write('### header ### \n')
for i_m in range(len(fit_logm)):
f.write('%f \t %f' % (fit_logm[i_m], fit_fsfms[i_m]))
f.write('\n')
f.close()
# best-fit quiescent fraction from Hahn et al. (2017)
f_Q_cen = lambda mm: -6.04 + 0.64 * mm
pretty_colors = prettycolors()
fig = plt.figure(figsize=(5,5))
sub = fig.add_subplot(111)
sub.scatter(fit_logm, 1. - fit_fsfms, lw=0, c=pretty_colors[1])
marr = np.linspace(9., 12., 100)
fsfms_bf = sub.plot(marr, 1-(coeff[0]*(marr-logMfid) + coeff[1]), c=pretty_colors[1], lw=2, ls='-')
fq = sub.plot(marr, f_Q_cen(marr), c=pretty_colors[3], lw=1.5, ls='--')
sub.set_xlim([9., 11.5])
sub.set_xticks([9., 10., 11.])
sub.set_xlabel('log$(\; M_*\; [M_\odot]\;)$', fontsize=25)
sub.set_ylim([0., 1.])
sub.set_ylabel('$1 - f_\mathrm{SFMS}$', fontsize=25)
legend1 = sub.legend(fsfms_bf, ['$1 - f^\mathrm{bestfit}_\mathrm{SFMS}$'], loc='upper left', prop={'size': 20})
sub.legend(fq, ['Hahn et al.(2017)\n $f_\mathrm{Q}^\mathrm{cen}(M_*, z\sim0)$'], loc='lower right', prop={'size': 15})
plt.gca().add_artist(legend1)
fig.savefig(''.join([UT.tex_dir(), 'figs/fq_fsfms.pdf']), bbox_inches='tight', dpi=150)
plt.close()
return None
def AllSubhaloSFMS(scatter=0.2, source='li-march', age_indicator=None):
'''
'''
# import AllSubhalo object
owl = AllSubhalos(scatter=scatter, source=source)
owl.Build(age_indicator=age_indicator)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(16,8))
if age_indicator is None:
sub1 = fig.add_subplot(121) # centrals
sub1.scatter(getattr(owl, 'm.star')[owl.cen_index], owl.sfr[owl.cen_index], c=pretty_colors[3], lw=0)
sub2 = fig.add_subplot(122) # satellites
sub2.scatter(getattr(owl, 'm.star')[owl.sat_index], owl.sfr[owl.sat_index], c=pretty_colors[5], lw=0)
else:
sub1 = fig.add_subplot(121) # centrals
cen = sub1.scatter(getattr(owl, 'm.star')[owl.cen_index], owl.sfr[owl.cen_index], c=owl.central_age, cmap='viridis', lw=0)
plt.colorbar(cen)
sub2 = fig.add_subplot(122) # satellites
sub2.scatter(getattr(owl, 'm.star')[owl.sat_index], owl.sfr[owl.sat_index], c=pretty_colors[5], lw=0)
sub1.text(9.5, -4., 'Central', fontsize=25)
sub2.text(9.5, -4., 'Satellite', fontsize=25)
sub1.set_xlim([9.0, 12.0])
sub1.set_xlabel(r'$\mathtt{M}_*$', fontsize=25)
sub1.set_ylim([-5.0, 2.0])
sub1.set_xlabel('SFR', fontsize=25)
sub2.set_xlim([9.0, 12.0])
sub2.set_xlabel(r'$\mathtt{M}_*$', fontsize=25)
sub2.set_ylim([-5.0, 2.0])
sub2.set_yticklabels([])
if age_indicator is None:
age_str = 'NOagematch'
else:
age_str = 'agematch_'+age_indicator
fig_file = ''.join(['figure/test/',
'AllSubhalosSFMS',
'.sham',
'.', str(scatter),
'.', source,
'.', age_str,
'.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def AllSubhalosSMF(scatter=0.2, source='li-march', age_indicator=None):
''' Compare the SMF of the AllSubhalo object to analytic SMF
'''
# import AllSubhalo object
owl = AllSubhalos(scatter=scatter, source=source)
owl.Build(age_indicator=age_indicator)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
smf = SMF()
# AllSubhalo SMF
owl.mass = getattr(owl, 'm.star')
subh_mass, subh_phi = smf.Obj(owl, dlogm=0.1, LF=False)
sub.plot(subh_mass, subh_phi, lw=4, c=pretty_colors[3])
subh_mass, subh_phi = smf._smf(owl.mass[np.where(owl.sfr != -999.)], dlogm=0.1)
sub.plot(subh_mass, subh_phi, lw=1, c='k')
sub.vlines(9.7, subh_phi.min(), subh_phi.max(), linestyle='--', color='k')
# Analytic SMF
analytic_mass, analytic_phi = smf.analytic(0.05, source=source)
sub.plot(analytic_mass, analytic_phi, lw=4, ls='--', c='k', label='Analytic')
# y-axis
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_ylabel(r'$\Phi$', fontsize=25)
# x-axis
sub.set_xlim([6.0, 12.0])
sub.set_xlabel(r'$\mathtt{M_*}$', fontsize=25)
sub.legend(loc='lower left')
if age_indicator is None:
age_str = 'NOagematch'
else:
age_str = 'agematch_'+age_indicator
fig_file = ''.join(['figure/test/',
'AllSubhalosSMF',
'.sham',
'.', str(scatter),
'.', source,
'.', age_str,
'.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
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 Test_Jackknife(n_jack, RADec_bins=[3, 3]):
''' Test the Jackknifing
'''
cat_dict = {
'name': 'tinker',
'Mrcut': 18,
'primary_delv': 500.,
'primary_rperp': 0.5,
'neighbor_delv': 500.,
'neighbor_rperp': 5.
}
# read conformity catalog based on input catalog dictionary
concat = clog.ConformCatalog(Mrcut=cat_dict['Mrcut'],
primary_delv=cat_dict['primary_delv'],
primary_rperp=cat_dict['primary_rperp'],
neighbor_delv=cat_dict['neighbor_delv'],
neighbor_rperp=cat_dict['neighbor_rperp'])
catalog = concat.Read()
jack_catalog = concat.Jackknife(catalog, n_jack, RADec_bins=RADec_bins)
#jack_catalog = concat.ReadJackknife(n_jack, RADec_bins=RADec_bins)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
sub.scatter(catalog['ra'], catalog['dec'], s=6, lw=0, c='k')
sub.scatter(jack_catalog['ra'],
jack_catalog['dec'],
s=6,
lw=0,
c=pretty_colors[3])
# axes
sub.set_xlabel('RA', fontsize=25)
sub.set_xlim([-50, 400])
sub.set_ylabel('Dec', fontsize=25)
sub.set_ylim([-20, 80])
sub.minorticks_on()
fig_file = ''.join([
UT.dir_fig(), 'test_jackknife.',
str(n_jack), 'of',
str(RADec_bins[0]), 'x',
str(RADec_bins[1]), '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def PlotkPk(name, version):
peek = PK.Pk(name, version)
peek.Read()
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
sub.plot(peek.k, peek.k * peek.p0k, lw=3, c=pretty_colors[3])
sub.set_xlabel('$\mathtt{k} (h/\mathtt{Mpc})$', fontsize=30)
sub.set_ylabel('$\mathtt{k P_0(k)}$', fontsize=30)
plt.show()
def IntegTest():
''' Test the convergence of integration methods by trying both euler and
RK4 integration for different time steps and then comparing the resulting SMF
Conclusions
-----------
* The integration is more or less converged after tstep <= 0.5 dt_min especially for
RK4. So for calculations use tstep = 0.5 dt_min.
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(15,6))
for ii, integ in enumerate(['euler', 'rk4']):
sub = fig.add_subplot(1,2,ii+1)
for i_t, tstep in enumerate([0.001, 0.01, 0.1]):
evol_dict = {
'sfh': {'name': 'random_step', 'dt_min': 0.1, 'dt_max': 0.5, 'sigma': 0.3,
'assembly_bias': 'acc_hist', 'halo_prop': 'frac', 'sigma_bias': 0.3},
'mass': {'type': integ, 'f_retain': 0.6, 't_step': tstep}
}
EGP = CMS.EvolvedGalPop(cenque='default', evol_dict=evol_dict)
if not os.path.isfile(EGP.File()):
EGP.Write()
EGP.Read()
MSonly = np.where(EGP.t_quench == 999.) # remove the quenching galaxies
smf = Obvs.getSMF(EGP.mass[MSonly], weights=EGP.weight_down[MSonly])
sub.plot(smf[0], smf[1], lw=3,
c=pretty_colors[i_t], ls='--',
label=''.join([integ, ';', r'$\mathtt{t_{step} =', str(tstep),'}$']))
# analytic SMF for comparison
theory_smf = Obvs.getSMF(EGP.M_sham[MSonly], weights=EGP.weight_down[MSonly])
sub.plot(theory_smf[0], theory_smf[1], lw=2, c='k', label='Theory')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([8., 12.0])
sub.set_yscale('log')
sub.set_xlabel(r'Mass $\mathtt{M_*}$', fontsize=25)
if ii == 0:
sub.set_ylabel(r'Stellar Mass Function $\mathtt{\Phi}$', fontsize=25)
sub.legend(loc='upper right', frameon=False)
fig_file = ''.join([UT.fig_dir(), 'IntegTest.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def plot_a_spectrum():
t_start = time.time()
sps = fsps.StellarPopulation(zcontinuous=1)
spec = sps.get_spectrum()
print time.time() - t_start, ' seconds' # takes 3 mins... wtf
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
for i in range(spec[1].shape[0]):
sub.plot(spec[0], 1.e17 * (spec[1][i, :]), c=pretty_colors[i % 10])
sub.set_xlabel(r'$\lambda$', fontsize=25)
sub.set_xlim([1000, 10000])
fig.savefig('aspectrum.png')
plt.close()
return None
def PlotABC_tQdelay(tf, cen_abcrun=None):
'''
'''
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))
m_arr = np.arange(7.5, 12.0, 0.1)
tdels = []
for i in xrange(len(theta)):
i_tqdelay_dict = {'name': 'explin', 'm': theta[i][0], 'b': theta[i][1]}
tdels.append(tDelay(m_arr, **i_tqdelay_dict))
tdels = np.array(tdels)
a, b, c, d, e = np.percentile(tdels, [2.5, 16, 50, 84, 97.5], axis=0)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(7,7))
sub = fig.add_subplot(111)
mstar = np.array([1.56781e+11, 1.01089e+11, 6.27548e+10, 3.98107e+10, 2.49831e+10, 1.57633e+10, 9.89223e+9, 6.37831e+9])
tqdel_high = np.array([1.36456e+0 , 2.53148e+0 , 3.06686e+0 , 3.52693e+0 , 3.62625e+0 , 3.99574e+0, 3.99915e+0 ,3.99915e+0])
tqdel_low = np.array([8.15728e-1, 1.98257e+0, 2.29240e+0, 2.82772e+0, 2.99466e+0, 2.72548e+0, 2.99016e+0, 2.68333e+0])
sub.fill_between(mstar, tqdel_low, tqdel_high, color=pretty_colors[0], edgecolor="none", label='Roughly Wetzel+(2013)')
#sub.plot(10**m_arr, tDelay(m_arr, **abc_tqdelay_dict), c=pretty_colors[3], label='ABC Best-fit')
sub.fill_between(10**m_arr, a, e, color=pretty_colors[3], alpha=0.5, edgecolor="none",
label='ABC Best-fit')
sub.fill_between(10**m_arr, b, d, color=pretty_colors[3], alpha=1., edgecolor="none")
sub.plot(10**m_arr, tDelay(m_arr, **{'name': 'hacked'}))
sub.set_ylim([0.0, 4.0])
sub.set_ylabel(r'$\mathtt{t_{Q, delay}}$', fontsize=25)
sub.set_xlim([5*10**9, 2*10**11.])
sub.set_xscale("log")
sub.set_xlabel(r'$\mathtt{M}_*$', fontsize=25)
sub.legend(loc='lower left')
fig.savefig('figure/sallite_tqdelay_'+cen_abcrun+'.png', bbox_inches='tight')
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 Test_KauffmannParent_Pssfr():
''' look at the P(sSFR) of the Kauffmann et al. sample
'''
catalog = clog.KauffmannParent()
is_notnan = np.isnan(catalog['ssfr_fib']) == False
fib_ssfr = catalog['ssfr_fib'][is_notnan]
print len(catalog['ssfr_fib']) - np.sum(is_notnan), ' out of ', len(
catalog['ssfr_fib']), ' galaxies have NaN SSFR'
ssfr_min, ssfr_max = -13., -8.75
pdf, bins = np.histogram(fib_ssfr,
range=[ssfr_min, ssfr_max],
bins=20,
normed=True)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
sub.plot(0.5 * (bins[:-1] + bins[1:]), pdf, c=pretty_colors[3], lw=3)
sub.text(-12.8,
0.5,
'median sSFR = ' + str(round(np.median(fib_ssfr), 3)),
fontsize=20)
sub.vlines(np.median(fib_ssfr),
0.,
10.,
color=pretty_colors[1],
linewidth=3,
linestyle='--')
sub.set_title('Kauffmann et al.(2013) cuts on dr72bright34')
# axes
sub.set_xlabel(r'log(SSFR)', fontsize=25)
sub.set_xlim([ssfr_min, ssfr_max])
sub.set_xticks([-13, -11, -9])
sub.set_ylabel(r'P(SSFR)', fontsize=25)
sub.set_ylim([0.0, 0.6])
sub.set_yticks([0., 0.2, 0.4, 0.6])
sub.minorticks_on()
fig_file = ''.join([UT.dir_fig(), 'KauffmannParent.Pssfr.png'])
fig.savefig(fig_file)
plt.close()
return None
def PlotLee2015_SFMS_zdep():
''' Plot the S0 term (redshift dependence term of the SFMS parmaterization)
of the Lee et al. (2015) SFMS fits.
'''
z_mid = np.array([0.36, 0.55, 0.70, 0.85, 0.99, 1.19])
S0 = np.array([0.80, 0.99, 1.23, 1.35, 1.53, 1.72])
S0_err = np.array([0.019, 0.015, 0.016, 0.014, 0.017, 0.024])
zslope, const = Lee2015_SFMS_zslope()
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
sub.errorbar(z_mid,
S0,
yerr=S0_err,
c='k',
elinewidth=3,
capsize=10,
label='Lee et al. (2015)')
sub.plot(z_mid,
zslope * (z_mid - 0.05) + const,
c=pretty_colors[1],
lw=2,
ls='--',
label='Best fit')
ztext = '\n'.join(
['Redshift slope', r'$\mathtt{A_z=' + str(round(zslope, 2)) + '}$'])
sub.text(0.1, 1.5, ztext, fontsize=20)
sub.legend(loc='lower right')
sub.set_ylabel('Lee et al. (2015) $S_0$ term', fontsize=25)
sub.set_xlim([0.0, 1.5])
sub.set_xlabel('Redshift $(\mathtt{z})$', fontsize=25)
fig_file = ''.join(['figure/', 'Lee2015_SFMS_zdep.png'])
fig.savefig(fig_file, bbox_inches='tight')
return None
def plot_spectrum_tage():
t_start = time.time()
sps = fsps.StellarPopulation(zcontinuous=1)
print time.time() - t_start, ' seconds' # takes 3 mins... wtf
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
for i, tage in enumerate(np.arange(1., 15., 2.)):
wave, spec = sps.get_spectrum(tage=tage)
sub.plot(wave,
1.e17 * spec,
c=pretty_colors[i],
label=r'$\mathtt{t_{age} = ' + str(tage) + '}$')
sub.set_xlabel(r'$\lambda$', fontsize=25)
sub.set_xlim([1000, 10000])
sub.set_xlabel(r'$f_\lambda$ (unknown scale)', fontsize=25)
sub.legend(loc='upper left')
fig.savefig('spectrum_tage.png', bbox_inches='tight')
plt.close()
return None
def ABCvsMCMC_contour(obvs, nwalkers=100, nburns=9000, sigma=False):
''' Plots that compare the ABC posteriors to the MCMC posteriors
'''
if obvs == 'nbargmf':
abc_dir = ''.join([
ut.dat_dir(),
'paper/ABC',
obvs,
'/run1/',
])
abc_theta_file = lambda tt: ''.join(
[abc_dir, 'nbar_gmf_theta_t',
str(tt), '.ABCnbargmf.dat'])
tf = 8
mcmc_dir = ''.join([ut.dat_dir(), 'paper/'])
mcmc_filename = ''.join([mcmc_dir, 'nbar_gmf.mcmc.mcmc_chain.p'])
elif obvs == 'nbarxi':
abc_dir = ''.join([
ut.dat_dir(),
'paper/ABC',
obvs,
'/',
])
abc_theta_file = lambda tt: ''.join(
[abc_dir, 'nbar_xi_theta_t',
str(tt), '.abc.dat'])
tf = 9
mcmc_dir = ''.join([ut.dat_dir(), 'paper/'])
mcmc_filename = ''.join([mcmc_dir, 'nbar_xi.mcmc.mcmc_chain.p'])
else:
raise ValueError
prior_min, prior_max = PriorRange('first_try')
prior_range = np.zeros((len(prior_min), 2))
prior_range[:, 0] = prior_min
prior_range[:, 1] = prior_max
# true HOD parameter
true_dict = Data.data_hod_param(Mr=21)
truths = [
true_dict['logM0'], # log M0
np.log(true_dict['sigma_logM']), # log(sigma)
true_dict['logMmin'], # log Mmin
true_dict['alpha'], # alpha
true_dict['logM1'] # log M1
]
mcmc_sample = pickle.load(open(mcmc_filename, 'rb'))[nburns * nwalkers:, :]
abc_sample = np.loadtxt(abc_theta_file(tf))
par_labels = [
r'$\mathtt{log}\;\mathcal{M}_{0}$',
r'$\mathtt{log}\;\sigma_\mathtt{log\;M}$',
r'$\mathtt{log}\;\mathcal{M}_\mathtt{min}$', r'$\alpha$',
r'$\mathtt{log}\;\mathcal{M}_{1}$'
]
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(20, 6))
gs = gridspec.GridSpec(1, 3)
# first panel
for i in [0, 1, 2]:
plot_range = np.zeros((2, 2))
if i == 0:
col_pair = [2, 3]
plot_range[0, 0] = 12.5
plot_range[0, 1] = 13.0
plot_range[1, 0] = prior_range[3, 0]
plot_range[1, 1] = prior_range[3, 1]
elif i == 2:
col_pair = [4, 2]
plot_range[0, 0] = 13.6
plot_range[0, 1] = 14.2
plot_range[1, 0] = 12.5
plot_range[1, 1] = 13.0
elif i == 1:
col_pair = [3, 4]
plot_range[0, 0] = prior_range[3, 0]
plot_range[0, 1] = prior_range[3, 1]
plot_range[1, 0] = 13.6
plot_range[1, 1] = 14.2
if i == 2:
mcmc_label = r'$\mathcal{L}^\mathtt{Gauss}$ MCMC'
abc_label = 'ABC-PMC'
else:
mcmc_label = None
abc_label = None
mcmc_par1 = mcmc_sample[:, col_pair[0]]
mcmc_par2 = mcmc_sample[:, col_pair[1]]
abc_par1 = abc_sample[:, col_pair[0]]
abc_par2 = abc_sample[:, col_pair[1]]
ax = plt.subplot(gs[i])
if sigma:
lvls = [1 - np.exp(-0.5), 1 - np.exp(-0.125)]
else:
lvls = [0.68, 0.95]
corner.hist2d(mcmc_par1,
mcmc_par2,
bins=20,
range=plot_range,
ax=ax,
plot_datapoints=False,
levels=lvls,
color='#1F77B4',
fill_contours=True,
smooth=1.0,
label=mcmc_label)
corner.hist2d(abc_par1,
abc_par2,
bins=20,
range=plot_range,
ax=ax,
levels=lvls,
color='#FF7F0E',
fill_contours=True,
smooth=1.0,
label=abc_label)
ax.scatter(np.repeat(truths[col_pair[0]], 2),
np.repeat(truths[col_pair[1]], 2),
s=100,
marker='*',
c='k',
lw=0,
label=None)
#ax.axvline(truths[i_col], color='k', ls='--', linewidth=3)
#ax.set_xticklabels([])
ax.set_xlim([plot_range[0, 0], plot_range[0, 1]])
ax.set_ylim([plot_range[1, 0], plot_range[1, 1]])
if i == 2:
thick_line1 = mlines.Line2D([], [],
ls='-',
c='#FF7F0E',
linewidth=12,
alpha=0.5,
label='ABC-PMC')
ax.legend(loc='upper right',
handles=[thick_line1],
frameon=False,
fontsize=25,
handletextpad=0.1,
scatteryoffsets=[0.5])
elif i == 1:
thick_line2 = mlines.Line2D(
[], [],
ls='-',
c='#1F77B4',
linewidth=12,
alpha=0.5,
label='$\mathcal{L}^\mathtt{Gauss}$ \nMCMC')
ax.legend(loc='upper right',
handles=[thick_line2],
frameon=False,
fontsize=25,
handletextpad=0.1,
scatteryoffsets=[0.5])
ax.set_xlabel(par_labels[col_pair[0]], fontsize=25, labelpad=15)
ax.set_ylabel(par_labels[col_pair[1]], fontsize=25)
if sigma:
sigma_str = '.true1sigma'
else:
sigma_str = ''
fig.subplots_adjust(wspace=0.3)
fig_name = ''.join([
ut.fig_dir(), 'paper.ABCvsMCMC.contour', '.', obvs, sigma_str, '.pdf'
])
fig.savefig(fig_name, bbox_inches='tight', dpi=150)
return None
def OrphanSubhaloSMF(type,
nsnap_ancestor=20,
scatter=0.0,
source='li-drory-march'):
''' SMF for orphan central subhalos that do not have ancestors at snapshot
nsnap_ancestor.
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
# Central subhalo at nsnap_ancestor
if type == 'central':
subh = CentralSubhalos()
elif type == 'all':
subh = Subhalos()
subh.Read(nsnap_ancestor,
scatter=scatter,
source=source,
nsnap_ancestor=nsnap_ancestor)
ancestor_index = subh.index
ancestor_mass = subh.mass
for i_snap in [1, 5, 10, 15]:
if i_snap >= nsnap_ancestor:
continue
smf = SMF()
if type == 'central':
subh = CentralSubhalos()
elif type == 'all':
subh = Subhalos()
subh.Read(i_snap,
scatter=scatter,
source=source,
nsnap_ancestor=nsnap_ancestor)
# total central subhalo SMF
subh_mass, subh_phi = smf.centralsubhalos(subh)
sub.plot(subh_mass, subh_phi, lw=4, c=pretty_colors[i_snap], alpha=0.5)
# SMF of central subhalos who's ancestors are centrals at snapshot 20
if i_snap == 1:
label = 'Galaxies that do not have Ancestors'
else:
label = None
orphan = np.invert(
np.in1d(getattr(subh, 'ancestor' + str(nsnap_ancestor)),
ancestor_index))
orph_mass, orph_phi = smf.smf(subh.mass[orphan])
sub.plot(orph_mass,
orph_phi,
lw=2,
ls='--',
c=pretty_colors[i_snap],
label=label)
anc_mass, anc_phi = smf.smf(ancestor_mass)
sub.plot(anc_mass, anc_phi, lw=4, c='gray', label='Total')
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([6.0, 12.0])
sub.legend(loc='upper right')
fig_file = ''.join([
'figure/test/'
'OrphanSubhaloSMF', '.', type, '_subhalo', '.scatter',
str(round(scatter, 2)), '.ancestor',
str(nsnap_ancestor), '.', source, '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def Plot_rhoSFR(source='li-march',
sfms_prop=None,
fq_lit='wetzel',
zslope=None):
''' Calculate the SFR density by integrating
rho_SFR = int n(M*) SFR_MS(M*) (1 - fQ(M*)) dM*
where n(M*) is given by the stellar mass function, SFR_MS is the SFMS SFR
fQ is the quiescent fraction
'''
if sfms_prop is None:
if zslope is None:
sfms_prop = {'name': 'linear', 'zslope': 1.5}
else:
sfms_prop = {'name': 'linear', 'zslope': zslope}
# SMF
smf_obj = gp.SMF()
n_smf = smf_obj.analytic
# SFMS Star Formation Rate
sfr_ms = sfr_evol.AverageLogSFR_sfms
# Quiescent Fraction
qf_obj = gp.Fq()
fq_model = qf_obj.model
def rhoSFR_integrand(Mstar, z):
M_smf, phi_smf = n_smf(z, source=source)
n_smf_M = interpolate.interp1d(M_smf, phi_smf)
return n_smf_M(Mstar) * 10.**sfr_ms(Mstar, z, sfms_prop=sfms_prop) * (
1. - fq_model(Mstar, z, lit=fq_lit))
z_range = np.array([0.1, 0.5, 1.0]) #np.arange(0.1, 1.0, 0.2)
rhoSFR_z = []
for zz in z_range:
print zz
rhoSFR_int_wrap = lambda mm: rhoSFR_integrand(mm, zz)
rhoSFR_int, rhoSFR_int_err = quad(rhoSFR_int_wrap,
7.5,
12.0,
epsrel=0.1)
rhoSFR_z.append(rhoSFR_int)
z_behroozi = [
0.036209892874743854, 0.40889165564288144, 0.6761600913028816,
0.9616372266749669, 1.170544445309055, 1.4822192825461054,
1.6049075298939122
]
sfr_cosmic = [
0.010063810047184728, 0.02228775362744163, 0.03802861399461939,
0.06050175265988725, 0.0805521892279678, 0.10063810047184728,
0.10862045113189879
]
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
sub.scatter(z_range, rhoSFR_z, s=10)
sub.plot(z_range, rhoSFR_z, c='k', lw=2)
sub.plot(z_behroozi, sfr_cosmic, lw=3, c='r', label='Behroozi Best-fit')
sub.set_xlabel(r'Redshift $\mathtt{(z)}$', fontsize=25)
sub.set_xlim([0.0, z_range.max()])
sub.set_ylim([0.001, 0.1])
sub.set_ylabel(r'$\rho_\mathtt{SFR}$', fontsize=25)
sub.set_yscale('log')
sub.legend(loc='upper left')
plt.show()
fig_name = ''.join([
'figure/test/', 'rhoSFR', '.SMF_', source, '.fq_', fq_lit,
'.SFMS_zslope',
str(round(sfms_prop['zslope'], 1)), '.png'
])
fig.savefig(fig_name, bbox_inches='tight')
plt.close()
return None
def test_ancestors_without_descendant(nsnap_ancestor=20,
scatter=0.0,
source='li-drory-march'):
'''
What happens to ancestors that do not have descendants at nsnap = 1
Notes
-----
* More than 50% of the subhalos at nsnap=20 do not have descendants at nsnap = 1.
What happens to them?
* Do they become satellites? --> Some become satellites, others
with no mass subhalos don't stay in the catalog at all
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
# Central subhalo at nsnap_ancestor (ancesotr)
anc = CentralSubhalos()
anc.read(nsnap_ancestor, scatter=scatter, source=source)
# Centrals subhalo at nsnap = 1 (descendant)
des = CentralSubhalos()
des.read(1, scatter=scatter, source=source)
no_descendants = np.invert(np.in1d(anc.index, des.ancestor20))
massive_nodescendants = np.where(anc.mass[no_descendants] > 0.0)
print 'N_SH no descendants ', len(anc.mass[no_descendants])
print 'N_SH total ', len(anc.mass)
print np.float(len(anc.mass[no_descendants])) / np.float(len(anc.mass))
no_des_index = anc.index[no_descendants][massive_nodescendants][:5]
print anc.mass[no_descendants][massive_nodescendants][:5]
for isnap in range(1, nsnap_ancestor)[::-1]:
i_des = CentralSubhalos()
i_des.read(isnap, scatter=scatter, source=source)
#print isnap, np.in1d(no_des_index, i_des.ancestor20)
#if not np.in1d(no_des_index, i_des.ancestor20)[0]:
des_sh = Subhalos()
des_sh.read(isnap, scatter=scatter, source=source)
#if np.sum(np.in1d(des_sh.ancestor20, no_des_index)) != len(no_des_index):
# raise ValueError
print des_sh.ilk[np.in1d(des_sh.ancestor20, no_des_index)][np.argsort(
des_sh.ancestor20[np.in1d(des_sh.ancestor20, no_des_index)])]
print 'M* ', des_sh.mass[np.in1d(
des_sh.ancestor20, no_des_index)][np.argsort(
des_sh.ancestor20[np.in1d(des_sh.ancestor20, no_des_index)])]
print 'M_halo ', getattr(des_sh, 'halo.m')[np.in1d(
des_sh.ancestor20,
no_des_index)][np.argsort(des_sh.ancestor20[np.in1d(
des_sh.ancestor20, no_des_index)])]
#print des_sh.mass[np.in1d(des_sh.index, no_des_index)]
#np.in1d(i_des.ancestor20, no_des_index)
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([6.0, 12.0])
sub.legend(loc='upper right')
def PosteriorObservable(Mr=21, b_normal=0.25, clobber=False):
''' Plot 1\sigma and 2\sigma model predictions from ABC-PMC posterior likelihood
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(16, 12))
gs = gridspec.GridSpec(2, 2, height_ratios=[2.5, 1], width_ratios=[1, 1])
for obvs in ['nbargmf', 'nbarxi']:
if obvs == 'nbargmf':
result_dir = ''.join([
ut.dat_dir(),
'paper/ABC',
obvs,
'/run1/',
])
theta_file = lambda tt: ''.join(
[result_dir, 'nbar_gmf_theta_t',
str(tt), '.ABCnbargmf.dat'])
tf = 8
obvs_list = ['gmf']
elif obvs == 'nbarxi':
result_dir = ''.join([ut.dat_dir(), 'paper/ABC', obvs, '/'])
theta_file = lambda tt: ''.join(
[result_dir, 'nbar_xi_theta_t',
str(tt), '.abc.dat'])
tf = 9
obvs_list = ['xi']
else:
raise ValueError
theta = np.loadtxt(theta_file(tf)) # import thetas
#theta = theta[:10]
obvs_file = ''.join(
theta_file(tf).rsplit('.dat')[:-1] + ['.', obvs_list[0], '.p'])
print obvs_file
HODsimulator = ABC_HODsim(Mr=Mr, b_normal=b_normal)
if not os.path.isfile(obvs_file) or clobber:
model_obv = []
for i in xrange(len(theta)):
print i
obv_i = HODsimulator(theta[i],
prior_range=None,
observables=obvs_list)
model_obv.append(obv_i[0])
model_obv = np.array(model_obv)
pickle.dump(model_obv, open(obvs_file, 'wb'))
else:
model_obv = pickle.load(open(obvs_file, 'rb'))
if 'xi' in obvs:
r_bin = Data.data_xi_bin(Mr=Mr)
elif 'gmf' in obvs:
r_binedge = Data.data_gmf_bins()
r_bin = 0.5 * (r_binedge[:-1] + r_binedge[1:])
a, b, c, d, e = np.percentile(model_obv, [2.5, 16, 50, 84, 97.5],
axis=0)
# plotting
if obvs == 'nbarxi':
ax = plt.subplot(gs[0])
elif obvs == 'nbargmf':
ax = plt.subplot(gs[1])
if 'xi' in obvs: # 2PCF
xi_data = Data.data_xi(Mr=Mr, b_normal=b_normal)
cov_data = Data.data_cov(Mr=Mr,
b_normal=b_normal,
inference='mcmc')
data_xi_cov = cov_data[1:16, 1:16]
ax.fill_between(r_bin,
a,
e,
color=pretty_colors[3],
alpha=0.3,
edgecolor="none")
ax.fill_between(r_bin,
b,
d,
color=pretty_colors[3],
alpha=0.5,
edgecolor="none")
ax.errorbar(r_bin,
xi_data,
yerr=np.sqrt(np.diag(data_xi_cov)),
fmt="o",
color='k',
markersize=0,
lw=0,
capsize=3,
elinewidth=1.5)
ax.scatter(r_bin, xi_data, c='k', s=10, lw=0)
ax.set_ylabel(r'$\xi_\mathtt{gg}(\mathtt{r})$', fontsize=27)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_xticklabels([])
ax.set_xlim([0.1, 20.])
ax.set_ylim([0.09, 1000.])
ax = plt.subplot(gs[2])
ax.fill_between(r_bin,
a / xi_data,
e / xi_data,
color=pretty_colors[3],
alpha=0.3,
edgecolor="none")
ax.fill_between(r_bin,
b / xi_data,
d / xi_data,
color=pretty_colors[3],
alpha=0.5,
edgecolor="none")
ax.errorbar(r_bin,
np.repeat(1., len(xi_data)),
yerr=np.sqrt(np.diag(data_xi_cov)) / xi_data,
fmt="o",
color='k',
markersize=0,
lw=0,
capsize=3,
elinewidth=1.5)
ax.plot(np.arange(0.1, 20., 0.1),
np.repeat(1., len(np.arange(0.1, 20, 0.1))),
c='k',
ls='--',
lw=2)
ax.set_xlim([0.1, 20.])
ax.set_xscale('log')
ax.set_ylim([0.5, 1.5])
ax.set_xlabel(r'$\mathtt{r}\;[\mathtt{Mpc}/h]$', fontsize=25)
ax.set_ylabel(r'$\xi_\mathtt{gg}/\xi_\mathtt{gg}^\mathtt{obvs}$',
fontsize=25)
elif 'gmf' in obvs: # GMF
data_gmf = Data.data_gmf(Mr=Mr, b_normal=b_normal)
cov_data = Data.data_cov(Mr=Mr,
b_normal=b_normal,
inference='mcmc')
data_gmf_cov = cov_data[16:, 16:]
ax.fill_between(r_bin,
a,
e,
color=pretty_colors[3],
alpha=0.3,
edgecolor="none")
ax.fill_between(r_bin,
b,
d,
color=pretty_colors[3],
alpha=0.5,
edgecolor="none",
label='ABC Posterior')
ax.errorbar(r_bin,
data_gmf,
yerr=np.sqrt(np.diag(data_gmf_cov)),
fmt="o",
color='k',
markersize=0,
lw=0,
capsize=4,
elinewidth=2)
ax.scatter(r_bin,
data_gmf,
s=15,
lw=0,
c='k',
label='Mock Observation')
ax.legend(loc='upper right',
scatterpoints=1,
prop={'size': 25},
borderpad=1.0)
ax.yaxis.tick_right()
ax.yaxis.set_ticks_position('both')
ax.yaxis.set_label_position('right')
ax.set_ylabel(r'$\zeta$ $[(\mathrm{h}/\mathtt{Mpc})^{3}]$',
fontsize=25)
ax.set_yscale('log')
ax.set_xlim([1., 20.])
ax.set_xticklabels([])
ax.set_ylim([10.**-7.2, 2 * 10**-4.])
ax = plt.subplot(gs[3])
ax.fill_between(r_bin,
a / data_gmf,
e / data_gmf,
color=pretty_colors[3],
alpha=0.3,
edgecolor="none")
ax.fill_between(r_bin,
b / data_gmf,
d / data_gmf,
color=pretty_colors[3],
alpha=0.5,
edgecolor="none")
ax.errorbar(r_bin,
np.repeat(1., len(data_gmf)),
yerr=np.sqrt(np.diag(data_gmf_cov)) / data_gmf,
fmt="o",
color='k',
markersize=0,
lw=0,
capsize=3,
elinewidth=1.5)
ax.plot(np.arange(1., 20., 1),
np.repeat(1., len(np.arange(1., 20, 1))),
c='k',
ls='--',
lw=1.75)
ax.yaxis.tick_right()
ax.yaxis.set_label_position('right')
ax.set_ylim([-0.1, 2.1])
ax.set_ylabel(r'$\zeta/\zeta^\mathtt{obvs}$', fontsize=25)
ax.set_xlim([1., 20.])
ax.set_xlabel(r'$\mathtt{N}$ [Group Richness]', fontsize=25)
fig.subplots_adjust(wspace=0.05, hspace=0.0)
fig_name = ''.join([ut.fig_dir(), 'paper', '.ABCposterior', '.pdf'])
fig.savefig(fig_name, bbox_inches='tight')
plt.close()
return None
def PoolEvolution(obvs):
''' Demostrative plot for the evolution of the pool. Illustrate the pool evolution for
log M_min versus log M_1, which has the starkest evolution from its prior.
'''
if obvs == 'nbargmf':
result_dir = ''.join([
ut.dat_dir(),
'paper/ABC',
obvs,
'/run1/',
])
theta_file = lambda tt: ''.join(
[result_dir, 'nbar_gmf_theta_t',
str(tt), '.ABCnbargmf.dat'])
t_list = [0, 1, 2, 3, 5, 8]
elif obvs == 'nbarxi':
result_dir = ''.join([ut.dat_dir(), 'paper/ABC', obvs, '/'])
theta_file = lambda tt: ''.join(
[result_dir, 'nbar_xi_theta_t',
str(tt), '.abc.dat'])
t_list = [0, 1, 2, 3, 7, 9]
else:
raise ValueError
prior_min, prior_max = PriorRange('first_try')
prior_range = np.zeros((2, 2))
prior_range[:, 0] = np.array([prior_min[2], prior_min[4]])
prior_range[:, 1] = np.array([prior_max[2], prior_max[4]])
# true HOD parameter
true_dict = Data.data_hod_param(Mr=21)
true_pair = [true_dict['logMmin'], true_dict['logM1']]
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(12, 8))
all_fig = fig.add_subplot(111)
for i_t, t in enumerate(t_list):
sub = fig.add_subplot(2, len(t_list) / 2, i_t + 1)
theta_Mmin, theta_M1 = np.loadtxt(theta_file(t),
unpack=True,
usecols=[2, 4])
corner.hist2d(theta_Mmin,
theta_M1,
bins=20,
range=prior_range,
levels=[0.68, 0.95],
color='c',
fill_contours=True,
smooth=1.0)
t_label = r"$\mathtt{t = " + str(t) + "}$"
sub.text(13.0, 15.0, t_label, fontsize=25)
if i_t == len(t_list) - 1:
true_label = r'$``\mathtt{true}"$'
else:
true_label = None
plt.scatter(np.repeat(true_pair[0], 2),
np.repeat(true_pair[1], 2),
s=75,
marker='*',
c='k',
lw=0,
label=true_label)
if i_t == len(t_list) - 1:
plt.legend(loc='lower left',
scatterpoints=1,
markerscale=2.5,
handletextpad=-0.25,
scatteryoffsets=[0.5])
if i_t == 0:
sub.set_xticklabels([])
sub.set_yticklabels([13., 13.5, 14., 14.5, 15., 15.5])
elif (i_t > 0) and (i_t < len(t_list) / 2):
sub.set_xticklabels([])
sub.set_yticklabels([])
elif i_t == len(t_list) / 2:
sub.set_yticklabels([13., 13.5, 14., 14.5, 15.])
elif i_t > len(t_list) / 2:
sub.set_yticklabels([])
all_fig.set_xticklabels([])
all_fig.set_yticklabels([])
all_fig.set_ylabel(r'$\mathtt{log}\;\mathcal{M}_\mathtt{1}$',
fontsize=30,
labelpad=50)
all_fig.set_xlabel(r'$\mathtt{log}\;\mathcal{M}_\mathtt{min}$',
fontsize=30,
labelpad=25)
fig.subplots_adjust(hspace=0.0)
fig_name = ''.join(
[ut.fig_dir(), 'paper_ABC_poolevolution', '.', obvs, '.pdf'])
fig.savefig(fig_name, bbox_inches='tight', dpi=150)
plt.close()
def ABC_Convergence(weighted=False):
''' Plot the error bars on the parameters as a function of time step
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(20, 10))
for i_obv, obvs in enumerate(['nbargmf', 'nbarxi']):
if obvs == 'nbargmf':
result_dir = ''.join([
ut.dat_dir(),
'paper/ABC',
obvs,
'/run1/',
])
theta_file = lambda tt: ''.join(
[result_dir, 'nbar_gmf_theta_t',
str(tt), '.ABCnbargmf.dat'])
w_file = lambda tt: ''.join(
[result_dir, 'nbar_gmf_w_t',
str(tt), '.ABCnbargmf.dat'])
tf = 8
elif obvs == 'nbarxi':
result_dir = ''.join([ut.dat_dir(), 'paper/ABC', obvs, '/'])
theta_file = lambda tt: ''.join(
[result_dir, 'nbar_xi_theta_t',
str(tt), '.abc.dat'])
w_file = lambda tt: ''.join(
[result_dir, 'nbar_xi_w_t',
str(tt), '.abc.dat'])
tf = 9
else:
raise ValueError
t_list = range(tf + 1)
columns = [
r"$\mathtt{log}\;\mathcal{M}_0$",
r"$\sigma_{\mathtt{log}\;\mathcal{M}}$",
r"$\mathtt{log}\;\mathcal{M}_\mathtt{min}$", r"$\alpha$",
r"$\mathtt{log}\;\mathcal{M}_1$"
]
true_dict = Data.data_hod_param(Mr=21)
true_theta = [
true_dict['logM0'],
np.log(true_dict['sigma_logM']), true_dict['logMmin'],
true_dict['alpha'], true_dict['logM1']
]
prior_min, prior_max = PriorRange('first_try')
a_theta = np.zeros((len(t_list), 5))
b_theta = np.zeros((len(t_list), 5))
d_theta = np.zeros((len(t_list), 5))
e_theta = np.zeros((len(t_list), 5))
for i_t, tt in enumerate(t_list):
theta_i = np.loadtxt(theta_file(tt), unpack=True)
w_i = np.loadtxt(w_file(tt))
for i_par in range(len(theta_i)):
if not weighted:
a, b, d, e = np.percentile(theta_i[i_par],
[2.5, 16, 84, 97.5],
axis=0)
else:
a = ut.quantile_1D(theta_i[i_par], w_i, 0.025)
b = ut.quantile_1D(theta_i[i_par], w_i, 0.16)
d = ut.quantile_1D(theta_i[i_par], w_i, 0.84)
e = ut.quantile_1D(theta_i[i_par], w_i, 0.975)
a_theta[i_t, i_par] = a
b_theta[i_t, i_par] = b
d_theta[i_t, i_par] = d
e_theta[i_t, i_par] = e
keep_index = [2, 3, 4]
for ii, i in enumerate(keep_index):
if i == keep_index[-1]:
true_label = r'$``\mathtt{true}"$'
abc_1sig_label = r'ABC Pool'
else:
true_label = None
abc_1sig_label = None
sub = fig.add_subplot(2, len(keep_index),
i_obv * len(keep_index) + ii + 1)
sub.fill_between(t_list,
a_theta[:, i],
e_theta[:, i],
color=pretty_colors[3],
alpha=0.3,
edgecolor="none")
sub.fill_between(t_list,
b_theta[:, i],
d_theta[:, i],
color=pretty_colors[3],
alpha=0.5,
edgecolor="none",
label=abc_1sig_label)
sub.plot(t_list,
np.repeat(true_theta[i], len(t_list)),
c='k',
ls='--',
lw=2,
label=true_label)
if ii == 0:
if obvs == 'nbargmf':
sub.text(4.85,
13.4,
r"$\bar{\mathtt{n}}$ and $\zeta(\mathtt{N})$",
fontsize=25)
elif obvs == 'nbarxi':
sub.text(4.85,
13.4,
r"$\bar{\mathtt{n}}$ and $\xi(\mathtt{r})$",
fontsize=25)
sub.set_ylabel(columns[i], fontsize=25)
sub.set_ylim([prior_min[i], prior_max[i]])
sub.set_xlim([-1, 10])
if i_obv == 1:
sub.legend(loc='upper right', borderpad=1.)
sub.set_xlabel('iterations', fontsize=25)
if i == 4:
sub.set_yticklabels([13.0, 13.5, 14.0, 14.5, 15.])
else:
sub.set_xticklabels([])
fig.subplots_adjust(wspace=0.3, hspace=0.0)
if weighted:
weight_str = '.weighted'
else:
weight_str = ''
fig_name = ''.join(
[ut.fig_dir(), 'paper', '.ABCconvergence', weight_str, '.pdf'])
fig.savefig(fig_name, bbox_inches='tight', dpi=150)
plt.close()
return None
def ABCvsMCMC_histogram(obvs, nwalkers=100, nburns=9000):
''' Plots that compare the ABC posteriors to the MCMC posteriors
'''
if obvs == 'nbargmf':
abc_dir = ''.join([
ut.dat_dir(),
'paper/ABC',
obvs,
'/run1/',
])
abc_theta_file = lambda tt: ''.join(
[abc_dir, 'nbar_gmf_theta_t',
str(tt), '.ABCnbargmf.dat'])
tf = 8
mcmc_dir = ''.join([ut.dat_dir(), 'paper/'])
mcmc_filename = ''.join([mcmc_dir, 'nbar_gmf.mcmc.mcmc_chain.p'])
elif obvs == 'nbarxi':
abc_dir = ''.join([
ut.dat_dir(),
'paper/ABC',
obvs,
'/',
])
abc_theta_file = lambda tt: ''.join(
[abc_dir, 'nbar_xi_theta_t',
str(tt), '.abc.dat'])
tf = 9
mcmc_dir = ''.join([ut.dat_dir(), 'paper/'])
mcmc_filename = ''.join([mcmc_dir, 'nbar_xi.mcmc.mcmc_chain.p'])
else:
raise ValueError
prior_min, prior_max = PriorRange('first_try')
prior_range = np.zeros((len(prior_min), 2))
prior_range[:, 0] = prior_min
prior_range[:, 1] = prior_max
# true HOD parameter
true_dict = Data.data_hod_param(Mr=21)
truths = [
true_dict['logM0'], # log M0
np.log(true_dict['sigma_logM']), # log(sigma)
true_dict['logMmin'], # log Mmin
true_dict['alpha'], # alpha
true_dict['logM1'] # log M1
]
mcmc_sample = pickle.load(open(mcmc_filename, 'rb'))[nburns * nwalkers:, :]
abc_sample = np.loadtxt(abc_theta_file(tf))
normie = norm()
sig1lo = normie.cdf(-1)
sig1hi = normie.cdf(1)
sig2lo = normie.cdf(-2)
sig2hi = normie.cdf(2)
nsamples_mcmc = len(mcmc_sample[:, 2])
nsamples_abc = len(abc_sample[:, 2])
sig1lo_mcmc = int(sig1lo * nsamples_mcmc)
sig2lo_mcmc = int(sig2lo * nsamples_mcmc)
sig1hi_mcmc = int(sig1hi * nsamples_mcmc)
sig2hi_mcmc = int(sig2hi * nsamples_mcmc)
sig1lo_abc = int(sig1lo * nsamples_abc)
sig2lo_abc = int(sig2lo * nsamples_abc)
sig1hi_abc = int(sig1hi * nsamples_abc)
sig2hi_abc = int(sig2hi * nsamples_abc)
par_labels = [
r'$\mathtt{log}\;\mathcal{M}_{0}$',
r'$\mathtt{log}\;\sigma_\mathtt{log\;M}$',
r'$\mathtt{log}\;\mathcal{M}_\mathtt{min}$', r'$\alpha$',
r'$\mathtt{log}\;\mathcal{M}_{1}$'
]
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(20, 8))
gs = gridspec.GridSpec(2, 3, height_ratios=[2.75, 1])
# first panel
for i in [0, 1, 2]:
if i == 0:
i_col = 2
prior_range[i_col, 0] = 12.5
prior_range[i_col, 1] = 13.
plot_range = prior_range[i_col, :]
elif i == 1:
i_col = 3
plot_range = prior_range[i_col, :]
elif i == 2:
i_col = 4
prior_range[i_col, 0] = 13.5
prior_range[i_col, 1] = 14.25
plot_range = np.array([13.5, 14.5])
ax = plt.subplot(gs[i])
q = ax.hist(mcmc_sample[:, i_col],
bins=20,
range=[prior_range[i_col, 0], prior_range[i_col, 1]],
normed=True,
alpha=0.75,
color=pretty_colors[1],
linewidth=2,
histtype='stepfilled',
edgecolor=None)
qq = ax.hist(abc_sample[:, i_col],
bins=20,
range=[prior_range[i_col, 0], prior_range[i_col, 1]],
normed=True,
alpha=0.75,
color=pretty_colors[3],
linewidth=2,
histtype='stepfilled',
edgecolor=None)
ax.axvline(truths[i_col], color='k', ls='--', linewidth=3)
ax.set_xticklabels([])
ax.set_xlim([plot_range[0], plot_range[1]])
if i == 2:
thick_line2 = mlines.Line2D(
[], [],
ls='-',
c=pretty_colors[1],
linewidth=4,
label='$\mathcal{L}^\mathtt{Gauss}$ \nMCMC')
thick_line1 = mlines.Line2D([], [],
ls='-',
c=pretty_colors[3],
linewidth=4,
label='ABC-PMC')
ax.legend(loc='upper right',
handles=[thick_line2, thick_line1],
frameon=False,
fontsize=25,
handletextpad=-0.0)
# general box properties
boxprops = {'color': 'k'}
medianprops = {'alpha': 0.}
bplots1 = []
ax = plt.subplot(gs[i + 3])
# stats dict for each box
bplots1.append({
'med': np.median(mcmc_sample[:, i_col]),
'q1': np.sort(mcmc_sample[:, i_col])[sig1lo_mcmc],
'q3': np.sort(mcmc_sample[:, i_col])[sig1hi_mcmc],
'whislo': np.sort(mcmc_sample[:, i_col])[sig2lo_mcmc],
'whishi': np.sort(mcmc_sample[:, i_col])[sig2hi_mcmc],
'fliers': []
})
bplots1.append({
'med': np.median(abc_sample[:, i_col]),
'q1': np.sort(abc_sample[:, i_col])[sig1lo_abc],
'q3': np.sort(abc_sample[:, i_col])[sig1hi_abc],
'whislo': np.sort(abc_sample[:, i_col])[sig2lo_abc],
'whishi': np.sort(abc_sample[:, i_col])[sig2hi_abc],
'fliers': []
})
whiskprop = dict(linestyle='-', linewidth=2, color='k')
boxprops = dict(linestyle='-', linewidth=2, color='k')
bxp1 = ax.bxp(bplots1,
positions=[1, 2],
vert=False,
patch_artist=True,
showfliers=False,
boxprops=boxprops,
medianprops=medianprops,
whiskerprops=whiskprop)
for ibox, box in enumerate(bxp1['boxes']):
if ibox == 0:
box.set(facecolor=pretty_colors[1], alpha=0.75)
elif ibox == 1:
box.set(facecolor=pretty_colors[3], alpha=0.75)
ax.axvline(truths[i_col], color='k', ls='--', linewidth=3)
ax.set_xlim([plot_range[0], plot_range[1]])
ax.set_xlabel(par_labels[i_col], fontsize=25, labelpad=15)
if i == 0:
ax.set_yticks([1, 2])
ax.set_yticklabels([r"$\mathtt{MCMC}$", r"$\mathtt{ABC}$"])
else:
ax.set_yticks([])
fig.subplots_adjust(wspace=0.2, hspace=0.0)
fig_name = ''.join([ut.fig_dir(), 'paper.ABCvsMCMC', '.', obvs, '.pdf'])
print fig_name
fig.savefig(fig_name, bbox_inches='tight', dpi=150)
return None
