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 plot_posterior_model(observable, abc_theta_file=None, data_dict={'Mr':20, 'b_normal': 0.25, 'Nmock':500},
clobber=False):
'''
Plot 1\sigma and 2\sigma model predictions from ABC-PMC posterior likelihood
Parameters
----------
observable : string
One of the following strings ['xi', 'scaledxi', 'gmf']
'''
# load the particles
if abc_theta_file is None:
raise ValueError("Please specify the theta output file from ABC-PMC run")
theta = np.loadtxt(abc_theta_file)
if observable == 'scaledxi':
obvs_str = 'xi'
else:
obvs_str = observable
obvs_file = ''.join(abc_theta_file.rsplit('.dat')[:-1] + ['.', observable, '.dat'])
print obvs_file
if not os.path.isfile(obvs_file) or clobber:
for i in xrange(len(theta)):
obv_i = HODsimulator(
theta[i], prior_range=None,
observables=[obvs_str], Mr=data_dict['Mr'])
try:
model_obv.append(obv_i[0])
except UnboundLocalError:
model_obv = [obv_i[0]]
model_obv = np.array(model_obv)
np.savetxt(obvs_file, model_obv)
else:
model_obv = np.loadtxt(obvs_file)
if 'xi' in observable:
r_bin = Data.data_xi_bins(Mr=data_dict['Mr'])
elif observable == 'gmf':
r_bin = Data.data_gmf_bins()
a, b, c, d, e = np.percentile(model_obv, [2.5, 16, 50, 84, 97.5], axis=0)
# plotting
fig = plt.figure(1)
ax = fig.add_subplot(111)
if observable == 'xi': # 2PCF
data_xi, data_xi_cov = Data.data_xi_full_cov(**data_dict) # data
ax.fill_between(r_bin, a, e, color="k", alpha=0.1, edgecolor="none")
ax.fill_between(r_bin, b, d, color="k", alpha=0.3, edgecolor="none")
#ax.plot(r_bin, c, "k", lw=1)
ax.errorbar(r_bin, data_xi, yerr = np.sqrt(np.diag(data_xi_cov)), fmt=".k",
capsize=0)
ax.set_xlabel(r'$r\;[\mathrm{Mpc}/h]$', fontsize=20)
ax.set_ylabel(r'$\xi_{\rm gg}$', fontsize=25)
ax.set_xscale('log')
ax.set_xlim([0.1, 20.])
elif observable == 'scaledxi': # Scaled 2PFC (r * xi)
data_xi, data_xi_cov = Data.data_xi_full_cov(**data_dict) # data
ax.fill_between(r_bin, r_bin*a, r_bin*e, color="k", alpha=0.1, edgecolor="none")
ax.fill_between(r_bin, r_bin*b, r_bin*d, color="k", alpha=0.3, edgecolor="none")
ax.errorbar(r_bin, r_bin*data_xi, yerr=r_bin*np.sqrt(np.diag(data_xi_cov)), fmt=".k",
capsize=0)
ax.set_xlabel(r'$r\;[\mathrm{Mpc}/h]$', fontsize=20)
ax.set_ylabel(r'$r \xi_{\rm gg}$', fontsize=25)
ax.set_xscale('log')
ax.set_xlim([0.1, 20.])
elif observable == 'gmf': # GMF
data_gmf, data_gmf_sigma = Data.data_gmf(**data_dict)
ax.fill_between(r_bin, a, e, color="k", alpha=0.1, edgecolor="none")
ax.fill_between(r_bin, b, d, color="k", alpha=0.3, edgecolor="none")
ax.errorbar(r_bin, data_gmf, yerr = data_gmf_sigma, fmt=".k",
capsize=0)
ax.set_xlabel(r'Group Richness', fontsize=25)
ax.set_ylabel(r'GMF $[\mathrm{h}^3\mathrm{Mpc}^{-3}]$', fontsize=25)
ax.set_yscale('log')
ax.set_xlim([1., 50.])
fig.savefig(
''.join([util.fig_dir(),
observable, '.posterior_prediction',
'.Mr', str(data_dict['Mr']), '_Nmock', str(data_dict['Nmock']),
'.pdf']),
bbox_inches='tight')
def plot_posterior_model(observable,
abc_theta_file=None,
data_dict={
'Mr': 20,
'b_normal': 0.25,
'Nmock': 500
},
clobber=False):
'''
Plot 1\sigma and 2\sigma model predictions from ABC-PMC posterior likelihood
Parameters
----------
observable : string
One of the following strings ['xi', 'scaledxi', 'gmf']
'''
# load the particles
if abc_theta_file is None:
raise ValueError(
"Please specify the theta output file from ABC-PMC run")
theta = np.loadtxt(abc_theta_file)
if observable == 'scaledxi':
obvs_str = 'xi'
else:
obvs_str = observable
obvs_file = ''.join(
abc_theta_file.rsplit('.dat')[:-1] + ['.', observable, '.dat'])
print obvs_file
if not os.path.isfile(obvs_file) or clobber:
for i in xrange(len(theta)):
obv_i = HODsimulator(theta[i],
prior_range=None,
observables=[obvs_str],
Mr=data_dict['Mr'])
try:
model_obv.append(obv_i[0])
except UnboundLocalError:
model_obv = [obv_i[0]]
model_obv = np.array(model_obv)
np.savetxt(obvs_file, model_obv)
else:
model_obv = np.loadtxt(obvs_file)
if 'xi' in observable:
r_bin = Data.data_xi_bins(Mr=data_dict['Mr'])
elif observable == 'gmf':
r_bin = Data.data_gmf_bins()
a, b, c, d, e = np.percentile(model_obv, [2.5, 16, 50, 84, 97.5], axis=0)
# plotting
fig = plt.figure(1)
ax = fig.add_subplot(111)
if observable == 'xi': # 2PCF
data_xi, data_xi_cov = Data.data_xi_full_cov(**data_dict) # data
ax.fill_between(r_bin, a, e, color="k", alpha=0.1, edgecolor="none")
ax.fill_between(r_bin, b, d, color="k", alpha=0.3, edgecolor="none")
#ax.plot(r_bin, c, "k", lw=1)
ax.errorbar(r_bin,
data_xi,
yerr=np.sqrt(np.diag(data_xi_cov)),
fmt=".k",
capsize=0)
ax.set_xlabel(r'$r\;[\mathrm{Mpc}/h]$', fontsize=20)
ax.set_ylabel(r'$\xi_{\rm gg}$', fontsize=25)
ax.set_xscale('log')
ax.set_xlim([0.1, 20.])
elif observable == 'scaledxi': # Scaled 2PFC (r * xi)
data_xi, data_xi_cov = Data.data_xi_full_cov(**data_dict) # data
ax.fill_between(r_bin,
r_bin * a,
r_bin * e,
color="k",
alpha=0.1,
edgecolor="none")
ax.fill_between(r_bin,
r_bin * b,
r_bin * d,
color="k",
alpha=0.3,
edgecolor="none")
ax.errorbar(r_bin,
r_bin * data_xi,
yerr=r_bin * np.sqrt(np.diag(data_xi_cov)),
fmt=".k",
capsize=0)
ax.set_xlabel(r'$r\;[\mathrm{Mpc}/h]$', fontsize=20)
ax.set_ylabel(r'$r \xi_{\rm gg}$', fontsize=25)
ax.set_xscale('log')
ax.set_xlim([0.1, 20.])
elif observable == 'gmf': # GMF
data_gmf, data_gmf_sigma = Data.data_gmf(**data_dict)
ax.fill_between(r_bin, a, e, color="k", alpha=0.1, edgecolor="none")
ax.fill_between(r_bin, b, d, color="k", alpha=0.3, edgecolor="none")
ax.errorbar(r_bin, data_gmf, yerr=data_gmf_sigma, fmt=".k", capsize=0)
ax.set_xlabel(r'Group Richness', fontsize=25)
ax.set_ylabel(r'GMF $[\mathrm{h}^3\mathrm{Mpc}^{-3}]$', fontsize=25)
ax.set_yscale('log')
ax.set_xlim([1., 50.])
fig.savefig(''.join([
util.fig_dir(), observable, '.posterior_prediction', '.Mr',
str(data_dict['Mr']), '_Nmock',
str(data_dict['Nmock']), '.pdf'
]),
bbox_inches='tight')
