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 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
