def cenque(self, cenque, **mkwargs):
'''
Plot stellar mass to halo mass ratio of input CenQue object
'''
if self.kwargs == {}:
kwargs = mkwargs.copy()
else:
kwargs = (self.kwargs).copy()
kwargs.update(mkwargs)
if 'label' in kwargs:
label = kwargs['label']
else:
label = 'z = ' + str(cenque.zsnap)
if 'color' in kwargs:
color = kwargs['color']
else:
try:
color = self.pretty_colors[cenque.nsnap]
except TypeError:
color = 'black'
bovy.scatterplot(cenque.halo_mass,
cenque.mass,
scatter=True,
color=color,
s=3,
xrange=[10.0, 15.0],
yrange=[9.0, 12.0],
ylabel='\mathtt{M_*}',
xlabel='\mathtt{M_{Halo}}')
return None
def cenque(self, cq_obj, scatter=False, bovyplot=True, **mkwargs):
'''
Plot SF-MS (M* vs SFR) for CenQue object
'''
if self.kwargs == {}:
self.kwargs = mkwargs.copy()
else:
self.kwargs = (self.kwargs).copy()
self.kwargs.update(mkwargs)
if 'label' in self.kwargs:
label = self.kwargs['label']
else:
label = 'z = ' + str(cq_obj.zsnap)
if 'color' in self.kwargs:
color = self.kwargs['color']
else:
try:
color = self.pretty_colors[cq_obj.nsnap]
self.cenque_nsnap = cq_obj.nsnap
except TypeError:
color = 'black'
if 'justsf' in self.kwargs:
if self.kwargs['justsf']:
justsf = np.where(cq_obj.gal_type == 'star-forming')
cq_mass = cq_obj.mass[justsf]
cq_sfr = cq_obj.sfr[justsf]
else:
cq_mass = cq_obj.mass
cq_sfr = cq_obj.sfr
else:
cq_mass = cq_obj.mass
cq_sfr = cq_obj.sfr
if scatter:
self.sub.scatter(cq_mass, cq_sfr, color=color, s=3)
self.sub.set_xlim([9.0, 12.0])
self.sub.set_ylim([-5.0, 2.0])
self.sub.set_xlabel(r'$\mathtt{M_*}$')
self.sub.set_ylabel(r'$\mathtt{log\;SFR}$')
self.scatter = True
else:
bovy.scatterplot(cq_mass,
cq_sfr,
scatter=True,
color=color,
s=3,
xrange=[9.0, 12.0],
yrange=[-5.0, 2.0],
xlabel='\mathtt{M_*}',
ylabel='\mathtt{log\;SFR}')
self.scatter = False
return None
def plot_mhalo_mstar(i_nsnap = 1, **kwargs):
''' Plot Mhalo versus M* of galaxies for CenQue file
'''
prettyplot()
pretty_colors = prettycolors()
# import evolved snapshot
if i_nsnap == 13:
snap = cq.CenQue()
snap.readin(nsnap=i_nsnap, file_type='sf assign', **kwargs)
else:
snap = cq.CenQue()
snap.readin(nsnap=i_nsnap, file_type='evol from 13', **kwargs)
mhalo = snap.halo_mass
mstar = snap.mass
bovy.scatterplot(mstar, mhalo, scatter=True, color=pretty_colors[1], s=3,
xrange=[9.0, 11.5],
xlabel='\mathtt{M_*}', ylabel='\mathtt{M_{Halo}}')
# figure file name ------------------------------------------------------------
# tau specifier
if kwargs['tau'] == 'discrete':
tau_str = '_'.join( [str("%.1f" % t) for t in kwargs['tau_param']] )+'tau'
elif kwargs['tau'] == 'linefit':
tau_str = '_'.join( [str("%.2f" % t) for t in kwargs['tau_param']] )+'tau'
else:
tau_str = kwargs['tau']+'tau'
# Stellar mass specifier
if kwargs['stellmass'].lower() == 'integrated':
mass_str = '_integ'
elif kwargs['stellmass'].lower() == 'sham':
mass_str = '_sham'
else:
raise NotImplementedError('asdfalkjlkjasdf')
# SFR specifier
if kwargs['sfr'] == 'sfr_avg':
file_type_str = mass_str+'_sfravg'
elif kwargs['sfr'] == 'sfr_func':
file_type_str = mass_str+'_sfrfunc'
else:
raise NotImplementedError('asdfasdflkjasdf;lkjasdf')
fig_name1 = ''.join(['figure/',
'cenque_mstar_mhalo_snapshot', str(i_nsnap), tau_str, file_type_str,
'_scatter.png'])
plt.savefig(fig_name1, bbox_inches='tight')
def plot_cenque_sfms(i_nsnap, **kwargs):
''' Plot SF-MS for CenQue
'''
prettyplot() #make things pretty
pretty_colors = prettycolors()
if i_nsnap < 13:
snap = cq.CenQue()
snap.readin(nsnap=i_nsnap, file_type='evol from 13', **kwargs)
else:
snap = cq.CenQue()
snap.readin(nsnap=13, file_type='sf assign', **kwargs)
sf_index = np.where(snap.gal_type == 'star-forming') # only keep star forming galaxies
mass = (snap.mass)[sf_index]
sfr = (snap.sfr)[sf_index]
# scatter plot with contours!
bovy.scatterplot(mass, sfr, scatter=True, color=pretty_colors[1], s=3,
xlabel=r'$\mathtt{M_*}$', ylabel=r'$\mathtt{SFR}$')
# figure file name ------------------------------------------------------------
# tau specifier
if kwargs['tau'] == 'discrete':
tau_str = '_'.join( [str("%.1f" % t) for t in kwargs['tau_param']] )+'tau'
elif kwargs['tau'] == 'linefit':
tau_str = '_'.join( [str("%.2f" % t) for t in kwargs['tau_param']] )+'tau'
else:
tau_str = kwargs['tau']+'tau'
# Stellar mass specifier
if kwargs['stellmass'].lower() == 'integrated':
mass_str = '_integ'
elif kwargs['stellmass'].lower() == 'sham':
mass_str = '_sham'
else:
raise NotImplementedError('asdfalkjlkjasdf')
# SFR specifier
if kwargs['sfr'] == 'sfr_avg':
file_type_str = mass_str+'_sfravg'
elif kwargs['sfr'] == 'sfr_func':
file_type_str = mass_str+'_sfrfunc'
else:
raise NotImplementedError('asdfasdflkjasdf;lkjasdf')
fig_name = ''.join(['figure/cenque_sfms', tau_str, file_type_str, '.png'])
plt.savefig(fig_name, bbox_inches='tight')
plt.show()
def test_photoz(catalog_name):
""" Test that the assigned photometric redshifts in photoz correction
method reproduce the CMASS photometric redshifts.
"""
catdict = {'name': catalog_name, 'n_mock': 20}
corrdict = {'name': 'photoz'}
cat_corr = {'catalog': catdict, 'correction': corrdict}
dataclass = Data('data', cat_corr)
dataclass.read()
# only consider galaxies with photometric redshifts
hasphotoz = np.where(
dataclass.photoz > -999.
)
deltaz_z = (dataclass.z[hasphotoz] - dataclass.photoz[hasphotoz]) / (1.0 + dataclass.z[hasphotoz])
prettyplot()
pretty_colors = prettycolors()
plt.figure(figsize=(8,8))
bovy.scatterplot(
dataclass.z[hasphotoz],
deltaz_z,
scatter = True,
levels = [0.68, 0.95, 0.997],
color = pretty_colors[1],
s = 3,
xrange = [0.43, 0.7],
yrange = [-0.3, 0.3],
xlabel = '\mathtt{z_{spec}}',
ylabel = r'\mathtt{\frac{|z_{spec} - z_{photo}|}{1 + z_{spec}}}'
)
plt.show()
# save figure to file
fig_dir = '/home/users/hahn/powercode/FiberCollisions/figure/'
fig_file = ''.join([
fig_dir,
catalog_name, '_deltaz_zspec_zphoto.png'
])
plt.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def plot_sfms_groupcat(Mrcut=18, writeout=True):
''' StarForming Main Sequence (mass vs SFR) of Star-Forming SDSS group catalog
Parameters
----------
Mrcut : Absolute magnitude cut off of SDSS group catalog
'''
# star-forming central galaxies from the SDSS group catalog
sf_cen = sf_centrals(Mrcut=Mrcut)
#prettyplot()
pretty_colors = prettycolors()
#mass_bin = util.simple_mass_bin() # mass bin
bovy.scatterplot(
sf_cen.mass,
sf_cen.sfr,
scatter = True,
levels = [0.68, 0.95, 0.997],
color = pretty_colors[1],
s = 3,
xrange = [9.5, 12.0],
yrange = [-1.5, 1.5],
xlabel = 'log \; M_{*}',
ylabel = 'log \; SFR'
)
#fig = plt.figure(figsize = (8,8))
#sub = fig.add_subplot(111)
#sub.hist2d(
# sf_cen.mass,
# sf_cen.sfr,
# bins = [30, 50]
# )
#sub.set_xlim([9.5, 12.0])
#sub.set_ylim([-1.5, 1.5])
#sub.set_xlabel(r'$\mathtt{log \; M_{*}}$', fontsize=20)
#sub.set_ylabel(r'$\mathtt{log \; SFR}$', fontsize=20)
if writeout:
fig_name = ''.join([
'figure/',
'sfms_SDSS_groupcat_', str(Mrcut), '.png'
])
plt.savefig(fig_name, bbox_inches='tight')
return None
def central_subhalos(self, centsub, **mkwargs):
'''
Plot the stellar mass to halo mass relation for CentralSubhalos object
'''
if self.kwargs == {}:
kwargs = mkwargs.copy()
else:
kwargs = (self.kwargs).copy()
kwargs.update(mkwargs)
if 'label' in kwargs:
label = kwargs['label']
else:
label = ''.join(
['z =',
str(round(util.get_z_nsnap(centsub.snapshot), 2))])
print label
if 'color' in kwargs:
color = kwargs['color']
else:
try:
color = self.pretty_colors[centsub.snapshot]
except TypeError:
color = 'black'
halo_mass = getattr(centsub, 'halo.m.max')
relevant_mstar_range = np.where(centsub.mass > 9.0)
#print len(halo_mass[relevant_mstar_range])
bovy.scatterplot(halo_mass[relevant_mstar_range],
centsub.mass[relevant_mstar_range],
scatter=True,
color=color,
s=3,
xrange=[10.0, 15.0],
yrange=[9.0, 12.0],
ylabel='\mathtt{M_*}',
xlabel='\mathtt{M_{Halo}}',
levels=[0.68, 0.95])
#self.subs.scatter(
# halo_mass,
# centsub.mass,
# color = color,
# label = label
# )
return None
def plot_sfms_groupcat(Mrcut=18, writeout=True):
''' StarForming Main Sequence (mass vs SFR) of Star-Forming SDSS group catalog
Parameters
----------
Mrcut : Absolute magnitude cut off of SDSS group catalog
'''
# star-forming central galaxies from the SDSS group catalog
sf_cen = sf_centrals(Mrcut=Mrcut)
#prettyplot()
pretty_colors = prettycolors()
#mass_bin = util.simple_mass_bin() # mass bin
bovy.scatterplot(sf_cen.mass,
sf_cen.sfr,
scatter=True,
levels=[0.68, 0.95, 0.997],
color=pretty_colors[1],
s=3,
xrange=[9.5, 12.0],
yrange=[-1.5, 1.5],
xlabel='log \; M_{*}',
ylabel='log \; SFR')
#fig = plt.figure(figsize = (8,8))
#sub = fig.add_subplot(111)
#sub.hist2d(
# sf_cen.mass,
# sf_cen.sfr,
# bins = [30, 50]
# )
#sub.set_xlim([9.5, 12.0])
#sub.set_ylim([-1.5, 1.5])
#sub.set_xlabel(r'$\mathtt{log \; M_{*}}$', fontsize=20)
#sub.set_ylabel(r'$\mathtt{log \; SFR}$', fontsize=20)
if writeout:
fig_name = ''.join(
['figure/', 'sfms_SDSS_groupcat_',
str(Mrcut), '.png'])
plt.savefig(fig_name, bbox_inches='tight')
return None
def plot(self, stellarmass=None, halomass=None, bovyplot=True, **mkwargs):
''' Plot SMHM ratio of input stellar mass and halo mass
'''
if stellarmass is None or halomass is None:
raise ValueError
if self.kwargs == {}:
kwargs = mkwargs.copy()
else:
kwargs = (self.kwargs).copy()
kwargs.update(mkwargs)
if 'label' in kwargs:
label = kwargs['label']
else:
label = None
if 'color' in kwargs:
if isinstance(kwargs['color'], str):
color = kwargs['color']
elif isinstance(kwargs['color'], int):
color = self.pretty_colors[kwargs['color']]
else:
color = 'black'
if bovyplot:
bovy.scatterplot(
halomass,
stellarmass,
scatter=True,
color=color,
s=3,
xrange=[10.0, 15.0],
yrange=[9.0, 12.0],
ylabel='\mathtt{M_*}',
xlabel='\mathtt{M_{Halo}}'
)
else:
self.sub.scatter(
halomass,
stellarmass,
color=color,
s=3,
label=label)
return None
def bloodline(self, bloodline, snapshot, **mkwargs):
'''
'''
descendant = getattr(bloodline, 'descendant_cq_snapshot'+str(snapshot))
if self.kwargs == {}:
kwargs = mkwargs.copy()
else:
kwargs = (self.kwargs).copy()
kwargs.update(mkwargs)
if 'label' in kwargs:
label = kwargs['label']
else:
label = ''.join([
'z =',
str(round(util.get_z_nsnap(descendant.nsnap), 2))
])
print label
if 'color' in kwargs:
color = kwargs['color']
else:
try:
color = self.pretty_colors[descendant.nsnap]
except TypeError:
color = 'black'
bovy.scatterplot(
descendant.mass,
descendant.sfr,
scatter=True,
color=color,
s=3,
xrange=[9.0, 12.0],
yrange=[-3.0, 3.0],
xlabel='\mathtt{M_*}',
ylabel='\mathtt{log\;SFR}'
)
return None
def plot_mhalo_mstar_snapshotSHAM(i_nsnap=1, scatter=0.0):
''' Plot M_halo versus SHAM stellar mass for TreePM --> SHAM snapshot
Parameters
----------
i_nsnap : snapshot #
scatter : set scatter of M_halo-M* in SHAM code
Notes
-----
* Essentially for testing TreePM and SHAM
'''
prettyplot()
pretty_colors = prettycolors()
# import TreePM-->SHAM output
if scatter == 0.0:
snapshot_file = ''.join(['dat/wetzel_tree/',
'subhalo_sham_centrals_snapshot', str(i_nsnap), '.hdf5'
])
else:
snapshot_file = ''.join(['dat/wetzel_tree/',
'subhalo_sham_centrals_snapshot', str(i_nsnap), '_scatter', str(scatter), '.hdf5'
])
print snapshot_file
f = h5py.File(snapshot_file, 'r') # read snapshot file
grp = f['cenque_data']
mhalo = grp['halo.m.max'][:]
mstar = grp['mass'][:]
bovy.scatterplot(mstar, mhalo, scatter=True, color=pretty_colors[1], s=3,
xrange = [9.0, 11.5],
xlabel=r'$\mathtt{M_{SHAM}}$', ylabel=r'$\mathtt{M_{Halo}}$')
fig_name = ''.join(['figure/',
'subhalo_sham_centrals_snapshot', str(i_nsnap), '_scatter', str(scatter), '.png'])
plt.savefig(fig_name, bbox_inches='tight')
def bloodline(self, bloodline, snapshot, **mkwargs):
'''
'''
descendant = getattr(bloodline,
'descendant_cq_snapshot' + str(snapshot))
if self.kwargs == {}:
kwargs = mkwargs.copy()
else:
kwargs = (self.kwargs).copy()
kwargs.update(mkwargs)
if 'label' in kwargs:
label = kwargs['label']
else:
label = ''.join(
['z =',
str(round(util.get_z_nsnap(descendant.nsnap), 2))])
print label
if 'color' in kwargs:
color = kwargs['color']
else:
try:
color = self.pretty_colors[descendant.nsnap]
except TypeError:
color = 'black'
bovy.scatterplot(descendant.mass,
descendant.sfr,
scatter=True,
color=color,
s=3,
xrange=[9.0, 12.0],
yrange=[-3.0, 3.0],
xlabel='\mathtt{M_*}',
ylabel='\mathtt{log\;SFR}')
return None
def plot_d_stellmass_snapshot(i_nsnap=1, **kwargs):
''' Plot the difference betwen integrated mass and SHAM mass versus SHAM mass for
CenQue model
'''
prettyplot()
pretty_colors = prettycolors()
# import evolved snapshot
if i_nsnap == 13:
snap = cq.CenQue()
snap.readin(nsnap=i_nsnap, file_type='sf assign', **kwargs)
else:
snap = cq.CenQue()
snap.readin(nsnap=i_nsnap, file_type='evol from 13', **kwargs)
gal_type = snap.gal_type
mass = (snap.mass)[gal_type == 'star-forming']
sham = (snap.sham_mass)[gal_type == 'star-forming']
bovy.scatterplot(sham, sham-mass, scatter=True, color=pretty_colors[1],
xlabel=r'$\mathtt{M_{SHAM}}$', ylabel=r'$\mathtt{\Delta M (M_{SHAM}- M_*)}$',
xrange=[9.0, 11.5])
# tau specifier
if kwargs['tau'] == 'discrete':
tau_str = '_'.join( [str("%.1f" % t) for t in kwargs['tau_param']] )+'tau'
elif kwargs['tau'] == 'linefit':
tau_str = '_'.join( [str("%.2f" % t) for t in kwargs['tau_param']] )+'tau'
else:
tau_str = kwargs['tau']+'tau'
# SFR specifier
file_type_str = kwargs['sfr']
fig_name = ''.join(['figure/',
'cenque_d_mstellmass_snapshot', str(i_nsnap), tau_str, file_type_str, '.png'])
plt.savefig(fig_name, bbox_inches='tight')
def qaplot_sfms_groupcat_fitting(Mrcut=18, sfq_test=True):
""" Function tests the SF-MS fitting functions and examines the
SF-MS of group catalog altogether as a sanity check.
If sfq_test is specified, then SFR(M*,z) cutoff for SF/Q classification
is plotted on top of the SF-MS plots
"""
# star-forming central galaxies from the SDSS group catalog
sf_cen = sf_centrals(Mrcut=Mrcut)
#prettyplot()
pretty_colors = prettycolors()
bovy.scatterplot(
sf_cen.mass,
sf_cen.sfr,
scatter = True,
levels = [0.68, 0.95, 0.997],
color = pretty_colors[1],
s = 3,
xrange = [9.5, 12.0],
yrange = [-1.5, 1.5],
xlabel = 'log \; M_{*}',
ylabel = 'log \; SFR'
)
# SDSS group catalog best fit
gc_zmid, gc_slope, gc_yint = get_bestfit_sfms_groupcat(Mrcut=Mrcut, clobber=True)
mass_bin = np.arange(9.0, 12.0, 0.25) # mass bin
plt.plot(
mass_bin,
gc_slope * (mass_bin-10.5) + gc_yint,
c='k',
lw=6,
ls='--'
)
# Envcount catalog best fit
ec_zmid, ec_slope, ec_yint = get_bestfit_sfms_envcount()
for i_z in xrange(len(ec_zmid)):
plt.plot(
mass_bin,
ec_slope[i_z] * (mass_bin-10.5) + ec_yint[i_z],
c = pretty_colors[i_z+3],
lw = 4,
label = str(ec_zmid[i_z])
)
avg_sfrs, sig_sfrs, ngals = get_sfr_mstar_z_groupcat(
mass_bin,
Mrcut=Mrcut
)
enough_gal = np.where(np.array(avg_sfrs) > -998)
plt.errorbar(
mass_bin[enough_gal],
np.array(avg_sfrs)[enough_gal],
yerr = np.array(sig_sfrs)[enough_gal],
lw = 4,
c = pretty_colors[1],
label='Average SFR'
)
if sfq_test:
plt.plot(mass_bin, sfr_cut(mass_bin, 0.05), c='k', lw=4, ls='--')
plt.legend(loc='lower right')
fig_name = ''.join(['figure/', 'qaplot_sfms_fitting_groupcat_', str(Mrcut), '.png'])
plt.savefig(fig_name, bbox_inches='tight')
plt.close()
def cenque(self, cq_obj, scatter=False, bovyplot=True, **mkwargs):
'''
Plot SF-MS (M* vs SFR) for CenQue object
'''
if self.kwargs == {}:
self.kwargs = mkwargs.copy()
else:
self.kwargs = (self.kwargs).copy()
self.kwargs.update(mkwargs)
if 'label' in self.kwargs:
label = self.kwargs['label']
else:
label = 'z = '+str(cq_obj.zsnap)
if 'color' in self.kwargs:
color = self.kwargs['color']
else:
try:
color = self.pretty_colors[cq_obj.nsnap]
self.cenque_nsnap = cq_obj.nsnap
except TypeError:
color = 'black'
if 'justsf' in self.kwargs:
if self.kwargs['justsf']:
justsf = np.where(cq_obj.gal_type == 'star-forming')
cq_mass = cq_obj.mass[justsf]
cq_sfr = cq_obj.sfr[justsf]
else:
cq_mass = cq_obj.mass
cq_sfr = cq_obj.sfr
else:
cq_mass = cq_obj.mass
cq_sfr = cq_obj.sfr
if scatter:
self.sub.scatter(
cq_mass,
cq_sfr,
color=color,
s=3
)
self.sub.set_xlim([9.0, 12.0])
self.sub.set_ylim([-5.0, 2.0])
self.sub.set_xlabel(r'$\mathtt{M_*}$')
self.sub.set_ylabel(r'$\mathtt{log\;SFR}$')
self.scatter = True
else:
bovy.scatterplot(
cq_mass,
cq_sfr,
scatter=True,
color=color,
s=3,
xrange=[9.0, 12.0],
yrange=[-5.0, 2.0],
xlabel='\mathtt{M_*}',
ylabel='\mathtt{log\;SFR}'
)
self.scatter = False
return None
def cmass_deltaz_zspec_zphoto_test(overplot=True):
''' Spherematch photometric and spectroscopic catalogs to
determine standard deviation redshift errors of photmetric redshifts
wrt spectroscopic redshifts
Parameters
----------
Notes
-----
* Using pyspherematch
* Details on CMASS redshifts in: https://trac.sdss3.org/wiki/BOSS/clustering/WGCatalogCode
* Assuming delta_z/z distribution is gaussian
'''
prettyplot() # set up plot
pretty_colors = prettycolors()
# matching spectroscopic and photometric redshifts
z_spec, z_photo = match_zspec_zphoto_cmass()
delta_z = (z_spec - z_photo) / (1.0 + z_spec)
if overplot:
bovy.scatterplot(
z_spec,
delta_z,
scatter=True,
levels=[0.68, 0.95, 0.997],
color=pretty_colors[1],
s=3,
xrange=[0.43, 0.7],
yrange=[-0.3, 0.3],
xlabel='\mathtt{z_{spec}}',
ylabel=r'\mathtt{\frac{|z_{spec} - z_{photo}|}{1\;+\;z_{spec}}}'
)
# summary statistics (mu and sigma) of delta z / (1+z)
z_mid, z_low, z_high, mu_deltaz, sigma_deltaz = cmass_deltaz_zspec_zphoto()
plt.errorbar(
z_mid,
mu_deltaz,
yerr=sigma_deltaz,
c=pretty_colors[3]
)
plt.scatter(
z_mid,
mu_deltaz,
c=pretty_colors[3]
)
fig_dir = '/home/users/hahn/powercode/FiberCollisions/figure/'
fig_file = ''.join([
fig_dir,
'cmass_deltaz_zspec_zphoto.png'
])
plt.savefig(
fig_file,
bbox_inches='tight'
)
plt.close()
def cmass_zspec_zphoto():
""" Compare matching spectroscopic redshifts and photometric redshifts
of the CMASS catalog.
"""
prettyplot()
pretty_colors = prettycolors()
# matching spectroscopic and photometric redshifts
z_spec, z_photo = match_zspec_zphoto_cmass()
# scatter plot of z_spec versus z_photo
bovy.scatterplot(
z_spec,
z_photo,
scatter=True,
color=pretty_colors[1],
s=3,
xrange=[0.0, 1.0],
yrange=[0.0, 1.0],
xlabel='\mathtt{z_{spec}}',
ylabel='\mathtt{z_{photo}}'
)
plt.plot(np.arange(0.0, 2.0, 0.1), np.arange(0.0, 2.0, 0.1), c='k', lw=4) # y = x
fig_dir = '/home/users/hahn/powercode/FiberCollisions/figure/'
fig_file = ''.join([
fig_dir,
'cmass_zspec_zphoto.png'
])
plt.savefig(
fig_file,
bbox_inches='tight'
)
plt.clf()
# scatter plot of delta z/(1+z) vs z
delta_z = (z_spec - z_photo) / (1.0 + z_spec)
bovy.scatterplot(
z_spec,
delta_z,
scatter=True,
levels=[0.68, 0.95, 0.997],
color=pretty_colors[1],
s=3,
xrange=[0.43, 0.7],
yrange=[-0.3, 0.3],
xlabel='\mathtt{z_{spec}}',
ylabel=r'\mathtt{\frac{|z_{spec} - z_{photo}|}{1\;+\;z_{spec}}}'
)
fig_file = ''.join([
fig_dir,
'cmass_delta_zphoto_zspec.png'
])
plt.savefig(
fig_file,
bbox_inches='tight'
)
return None
def plot(self, mass=None, sfr=None, sfr_class=None,
gal_class='star-forming', bovyplot=True, sigSFR=True, **mkwargs):
''' Plot SFR as a function of stellar mass for star-forming
galaxies
'''
if mass is None or sfr is None:
raise ValueError
if sfr_class is None and gal_class != 'all':
raise ValueError
if self.kwargs == {}:
self.kwargs = mkwargs.copy()
else:
self.kwargs = (self.kwargs).copy()
self.kwargs.update(mkwargs)
# label
if 'label' in self.kwargs:
label = self.kwargs['label']
else:
label = None
# color
if 'color' in self.kwargs:
if isinstance(self.kwargs['color'], str):
color = self.kwargs['color']
elif isinstance(self.kwargs['color'], int):
color = self.pretty_colors[self.kwargs['color']]
else:
color = 'black'
if gal_class != 'all':
just_gal_class = np.where(sfr_class == gal_class)
mass = mass[just_gal_class]
sfr = sfr[just_gal_class]
if not bovyplot:
self.sub.scatter(mass, sfr, color=color, s=3, label=label)
if sigSFR:
mbin = np.arange(7.0, 12.25, 0.25)
mlow = mbin[:-1]
mhigh = mbin[1:]
muSFR, sigSFR = [], []
for im in range(len(mlow)):
mbin = np.where((mass > mlow[im]) & (mass <= mhigh[im]))
muSFR.append(np.mean(sfr[mbin]))
sigSFR.append(np.std(sfr[mbin]))
self.sub.plot(0.5*(mlow+mhigh), muSFR, color='k', ls='--', lw=4)
self.sub.errorbar(0.5*(mlow+mhigh), muSFR, yerr=sigSFR, color='k')
self.sub.set_xlim([9.0, 12.0])
self.sub.set_ylim([-5.0, 2.0])
self.sub.set_xlabel(r'$\mathtt{log\;M_*}$', fontsize=25)
self.sub.set_ylabel(r'$\mathtt{log\;SFR}$', fontsize=25)
else:
bovy.scatterplot(mass, sfr, scatter=True, color=color,
s=3,
levels=[0.68, 0.95, 0.997], #1,2,3 sigmas
xrange=[9.0, 12.0],
yrange=[-5.0, 2.0],
xlabel='\mathtt{M_*}',
ylabel='\mathtt{log\;SFR}'
)
#plt.plot(np.arange(9.0, 12.0, 0.1),
# sfr_cut(np.arange(9.0, 12.0, 0.1), cq_obj.zsnap),
# ls='--', lw=3, c='r')
return None
def qaplot_sfms_envcount_fitting(Mrcut=18, sfq_test=True):
""" Test functions of the SF-MS module that fits the group catalog SFMS
If sfq_test is specified, then SFR(M*,z) cutoff for SF/Q classification
is plotted on top of the SF-MS plots
"""
#prettyplot()
pretty_colors = prettycolors()
# read SF galaxies from the envcount catalog
file_dir = 'dat/wetzel_tree/envcount/'
sdss_envcount_file = ''.join([file_dir,
'envcount_cylr2.5h35_thresh75_sdss_active_z0.05_0.12_primuszerr.fits'])
sdss_sf_data = mrdfits(sdss_envcount_file)
primus_envcount_file = ''.join([file_dir,
'envcount_cylr2.5h35_thresh75_active_z0.2_1.0_lit.fits'])
primus_sf_data = mrdfits(primus_envcount_file)
for i_z, z_mid in enumerate([0.1, 0.3, 0.5, 0.7, 0.9]):
if z_mid < 0.2:
sf_data = sdss_sf_data
# impose isolation criteria, mass completeness limit (should I ?) and edge cuts
centrals = np.where(
(sf_data.envcount == 0.0) &
(sf_data.mass > sf_data.masslimit) &
(sf_data.edgecut == 1)
)
else:
sf_data = primus_sf_data
# impose isolation criteria, mass completeness limit (should I ?) and edge cuts
centrals = np.where(
(sf_data.redshift >= z_mid - 0.1) &
(sf_data.redshift < z_mid + 0.1) &
(sf_data.envcount == 0.0) &
(sf_data.mass > sf_data.masslimit) &
(sf_data.edgecut == 1)
)
print sf_data.weight[centrals]
bovy.scatterplot(
sf_data.mass[centrals],
sf_data.sfr[centrals],
scatter = True,
levels = [0.68, 0.95, 0.997],
weights = sf_data.weight[centrals],
s = 3,
xrange = [9.5, 12.0],
yrange = [-1.5, 1.5],
xlabel = 'log \; M_{*}',
ylabel = 'log \; SFR'
)
#color = pretty_colors[1],
# SDSS group catalog best fit
gc_zmid, gc_slope, gc_yint = get_bestfit_sfms_groupcat(Mrcut=Mrcut, clobber=True)
mass_bin = np.arange(9.0, 12.0, 0.25) # mass bin
plt.plot(
mass_bin,
gc_slope * (mass_bin-10.5) + gc_yint,
c='k',
lw=6,
ls='--'
)
# Envcount catalog best fit
ec_zmid, ec_slope, ec_yint = get_bestfit_sfms_envcount(clobber=True)
plt.plot(
mass_bin,
ec_slope[i_z] * (mass_bin-10.5) + ec_yint[i_z],
c = pretty_colors[i_z+3],
lw = 4,
label = str(ec_zmid[i_z])
)
avg_sfrs, sig_sfrs, ngals = get_sfr_mstar_z_envcount(
mass_bin,
[z_mid for i in xrange(len(mass_bin))]
)
enough_gal = np.where(np.array(avg_sfrs) > -998)
plt.errorbar(
mass_bin[enough_gal],
np.array(avg_sfrs)[enough_gal],
yerr = np.array(sig_sfrs)[enough_gal],
lw = 4,
c = pretty_colors[1],
label='Average SFR'
)
plt.legend(loc='lower right')
if sfq_test:
plt.plot(mass_bin, sfr_cut(mass_bin, z_mid), c='k', lw=4, ls='--')
fig_name = ''.join(['figure/', 'qaplot_sfms_fitting_envcount_z', str(z_mid), '.png'])
plt.savefig(fig_name, bbox_inches='tight')
plt.close()
return None