Fe = euclid.planck_prior_full
F_eucl = euclid.euclid_to_rf(Fe, cosmo)
Fpl, lbls = rf.add_fisher_matrices(F,
F_eucl,
lbls,
l2,
expand=True)
# Get indices of w0, wa
pw0 = lbls.index('w0')
pwa = lbls.index('wa')
pA = lbls.index('A')
# Calculate FOM
cov_pl = np.linalg.inv(Fpl)
fom = rf.figure_of_merit(pw0, pwa, None, cov=cov_pl)
fom_values_expt.append(fom)
#fom_values_expt.append(1./np.sqrt(cov_pl[pA,pA])) # FIXME
print "%s: FOM = %3.2f, sig(A) = %3.3f" % (names[k], fom,
np.sqrt(cov_pl[pA, pA]))
print ">>> Paramname:", snames[j], " -- val:", param_values_expt[v]
# Sort values
idxs = param_values_expt.argsort()
param_values_expt = param_values_expt[idxs]
fom_values_expt = np.array(fom_values_expt)[idxs]
# Plot line for this parameter
line = ax.plot(param_values_expt / fac[j],
fom_values_expt / np.max(fom_values_expt),
label=names[k],
exclude=fixed_params)
# Get indices of w0, wa
pw0 = lbls.index('w0')
pwa = lbls.index('wa')
print "-" * 50
print names[k]
print "-" * 50
# Invert matrix
cov_pl = np.linalg.inv(Fpl)
# Print 1D marginals
print "1D sigma(w_0) = %3.4f" % np.sqrt(cov_pl[pw0, pw0])
print "1D sigma(w_a) = %3.4f" % np.sqrt(cov_pl[pwa, pwa])
print "FOM:", rf.figure_of_merit(pw0, pwa, Fpl, cov=cov_pl)
print lbls
x = rf.experiments.cosmo['w0']
y = rf.experiments.cosmo['wa']
print "FOM:", rf.figure_of_merit(pw0, pwa, Fpl, cov=cov_pl)
# Plot contours for gamma, w0
transp = [1., 0.85]
w, h, ang, alpha = rf.ellipse_for_fisher_params(pw0,
pwa,
None,
Finv=cov_pl)
ellipses = [
matplotlib.patches.Ellipse(xy=(x, y),
width=alpha[kk] * w,
if len(fixed_params) > 0:
Fpl, lbls = rf.combined_fisher_matrix( [Fpl,], expand=[],
names=lbls, exclude=fixed_params )
# Really hopeful H0 prior
#ph = lbls.index('h')
#Fpl[ph, ph] += 1./(0.012)**2.
# Invert matrix
cov_pl = np.linalg.inv(Fpl)
# Plot improvement in chosen parameter
if param1 == 'fom':
p1 = lbls.index('w0'); p2 = lbls.index('wa')
_fom = rf.figure_of_merit(p1, p2, None, cov=cov_pl)
else:
pp = lbls.index(param1)
_fom = 1. / np.sqrt(cov_pl[pp, pp])
zmax.append(zc[:l][-1])
fom.append(_fom)
# Plot curve for this experiment
line = ax.plot(zmax, fom, color=colours[k], ls='solid', lw=1.8,
marker='o', label=labels[k])
line[0].set_dashes(linestyle[k])
# Report on what options were used
print "-"*50
s1 = "Marginalised over Omega_K" if MARGINALISE_CURVATURE else "Fixed Omega_K"
exclude=excl)
# Add Planck prior
Fpl = euclid.add_planck_prior(F, lbls, info=False)
print "-" * 50
print names[k]
print "-" * 50
# Invert matrices
pns = rf.indexes_for_sampled_fns(3, zc.size, zfns)
pmnu = rf.indexes_for_sampled_fns(6, zc.size, zfns)
cov_pl = np.linalg.inv(Fpl)
print lbls[pns], lbls[pmnu]
fom = rf.figure_of_merit(pns, pmnu, None, cov=cov_pl)
print "%s: FOM = %3.2f" % (names[k], fom)
x = rf.experiments.cosmo['n']
y = 0.1 #rf.experiments.cosmo['wa']
# Plot contours for n_s, Mnu
w, h, ang, alpha = rf.ellipse_for_fisher_params(pns,
pmnu,
None,
Finv=cov_pl)
ellipses = [
matplotlib.patches.Ellipse(xy=(x, y),
width=alpha[kk] * w,
height=alpha[kk] * h,
angle=ang,
