def DataSummary(Mrcut=18, observables=['ssfr']):
''' Summary statistics of the data. In our case that is the
SSFR distribution of the SDSS group catalog.
'''
obvs = []
if 'ssfr' in observables:
# Group Catalog object
groupcat = GroupCat(Mrcut=Mrcut, position='central')
# SSFR distribution of group catalog
bins, dist = groupcat.Ssfr()
obvs.append([np.array(bins), np.array(dist)])
if 'fqz03' in observables:
qfrac = Fq()
M_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_mid = 0.5 * (M_bin[:-1] + M_bin[1:])
fq_model = qfrac.model(M_mid, 0.3412, lit='wetzel')
obvs.append([M_mid, fq_model])
if 'fqz_multi' in observables:
qfrac = Fq()
M_bin = np.array([9.7, 10.1, 10.5, 10.9, 11.3])
M_mid = 0.5 * (M_bin[:-1] + M_bin[1:])
fq_out = [M_mid]
for zz in [0.0502, 0.1581, 0.3412, 1.0833]:
fq_model = qfrac.model(M_mid, zz, lit='wetzel')
fq_out += [fq_model]
obvs.append(fq_out)
if len(observables) == 1:
obvs = obvs[0]
return obvs
def GroupCat(self, Mrcut=18, position='central', **kwargs):
''' Plot sSFR distribution for Group Catalog data
'''
groupcat = GroupCat(Mrcut=Mrcut, position=position)
ssfr_bin_mid, ssfr_hist = groupcat.Ssfr()
# loop through each panel
for i_mass, panel_mass in enumerate(self.panel_mass_bins):
if Mrcut == 18:
z_med = 0.03
elif Mrcut == 19:
z_med = 0.05
elif Mrcut == 20:
z_med = 0.08
ssfr_label= ''.join([
r'SDSS $\mathtt{M_r =', str(Mrcut), '}$'])
if 'lw' in kwargs.keys():
lwid = kwargs['lw']
else:
lwid = 4
if 'ls' in kwargs.keys():
lsty = kwargs['ls']
else:
lsty = '--'
if 'color' in kwargs.keys():
col = kwargs['color']
else:
col = 'k'
self.subs[i_mass].plot(
ssfr_bin_mid[i_mass],
ssfr_hist[i_mass],
color = col,
lw = lwid,
ls = lsty,
label = ssfr_label)
return None