def load_fisher_data(froot):
"""
Load Fisher forecast data.
"""
f_fisher = "%s.dat" % froot
f_zbins = "%s.zbins" % froot
# Load header
f = open(f_fisher, 'r')
hdr = f.readline()
f.close()
names = hdr[2:-1].split(' ')
# Load Fisher matrix and bin centre redshifts
F = np.genfromtxt(f_fisher)
zbinc = np.genfromtxt(f_zbins)
# Calculate mean values of H and D_A in LCDM model
abinc = 1. / (1. + zbinc)
#plcdm = model.pdict(h=0.6731, omegaM=0.315, omegaK=0.0, omegaB=0.045,
# w0=-1., winf=-1., zc=1e5, deltaz=0.5)
plcdm = model.pdict(h=0.6727,
omegaM=0.3166,
omegaK=0.0,
omegaB=0.04941,
w0=-1.,
winf=-1.,
zc=1e5,
deltaz=0.5)
# Convert units to same as Fisher matrix: H ~ (100 km/s/Mpc); D_A ~ Gpc
Hc = model.Hz(abinc, plcdm) / 1e2 # 100 km/s/Mpc
DAc = model.DAz(abinc, plcdm) / 1e3 # Gpc
# Construct mean vector and list of parameter names
mean_vec = np.concatenate((Hc, DAc))
lbls = ["H%d" % i for i in range(zbinc.size)] \
+ ["DA%d" % i for i in range(zbinc.size)]
# Invert Fisher matrix to get the covariance
cov = np.linalg.inv(F)
# Select only certain parameters from the covariance
new_cov = np.zeros((len(lbls), len(lbls)))
for i, pni in enumerate(lbls):
for j, pnj in enumerate(lbls):
new_cov[i, j] = cov[names.index(pni), names.index(pnj)]
# Return the inverse covariance for this subset of parameters
icov = np.linalg.inv(new_cov)
return zbinc, mean_vec, icov
# Save results
np.save("%s.pctcache" % fn, pct)
# Plot 95% percentiles
lbl = labels[j]
print(lbl)
if j == 0:
P.fill_between(z, pct[0], pct[-1], color=colours[j], alpha=0.6,
label=lbl, lw=2.5)
else:
dashes = [1.5,1.5] if dash[j] else []
P.plot(z, pct[0], color=colours[j], lw=LW, label=lbl, dashes=dashes)
P.plot(z, pct[-1], color=colours[j], lw=LW, dashes=dashes)
# LCDM curves
plcdm = model.pdict(h=0.6727, omegaM=0.3166, omegaK=0.0, omegaB=0.04941,
w0=-1., winf=-1., zc=1e5, deltaz=0.5)
OmegaDE0 = 1. - plcdm['omegaM'] - model.omegaR(plcdm) - 0.
P.plot(z, OmegaDE0*model.omegaDE(a, plcdm), 'k-', lw=1.8, alpha=1.)
#P.plot(z, plcdm['omegaM'] * a**-3., 'k-', lw=1.8)
#P.plot(z, 0.10 * plcdm['omegaM'] * a**-3., 'k--', lw=1.8, alpha=0.4)
#P.plot(z, 0.01 * plcdm['omegaM'] * a**-3., 'k--', lw=1.8, alpha=0.4)
#P.text(0.1, 0.17, r"$1\% \times \rho_M(z)/\rho_{{\rm crit}, 0}$", fontsize=13, alpha=0.85)
if MODE == 'lowz':
P.xlim((0., 2.2))
P.ylim((0.5, 0.9))
P.gca().xaxis.set_major_locator(ticker.MultipleLocator(0.5))
pct[0],
pct[-1],
color=colours[j],
alpha=0.6,
label=lbl,
lw=2.5)
else:
dashes = [1.5, 1.5] if 'DESI' in lbl else []
P.plot(z, pct[0], color=colours[j], lw=2.5, label=lbl, dashes=dashes)
P.plot(z, pct[-1], color=colours[j], lw=2.5, dashes=dashes)
# LCDM curves
plcdm = model.pdict(h=0.6731,
omegaM=0.315,
omegaK=0.0,
omegaB=0.045,
w0=-1.,
winf=-1.,
zc=1e5,
deltaz=0.5)
OmegaDE0 = 1. - plcdm['omegaM'] - model.omegaR(plcdm) - 0.
P.plot(z, OmegaDE0 * model.omegaDE(a, plcdm), 'k-', lw=1.8, alpha=1.)
#P.plot(z, plcdm['omegaM'] * a**-3., 'k-', lw=1.8)
#P.plot(z, 0.10 * plcdm['omegaM'] * a**-3., 'k--', lw=1.8, alpha=0.4)
#P.plot(z, 0.01 * plcdm['omegaM'] * a**-3., 'k--', lw=1.8, alpha=0.4)
#P.text(0.1, 0.17, r"$1\% \times \rho_M(z)/\rho_{{\rm crit}, 0}$", fontsize=13, alpha=0.85)
P.xlim((0., 7.))
P.ylim((0., 1.8))
DAz = np.array(DAz)
wz = np.array(wz)
pcts = [2.5, 16., 50., 84., 97.5]
print(dat['h'].size)
pct = np.percentile(Hz / (1. + z), pcts, axis=0)
pct_da = np.percentile(DAz, pcts, axis=0)
pct_wz = np.percentile(wz, pcts, axis=0)
#for i in range(pct.shape[0]):
# P.plot(z, pct[i], 'b-', lw=1.8, alpha=1. - np.abs(pcts[i] - 50.)/70.)
plcdm = model.pdict(h=0.6727,
omegaM=0.3166,
omegaK=0.0,
omegaB=0.04941,
w0=-1.,
winf=-1.,
zc=1e5,
deltaz=0.5)
#OmegaDE0 = 1. - plcdm['omegaM'] - model.omegaR(plcdm) - omegaK
#P.plot(z, OmegaDE0*model.omegaDE(a, plcdm), 'k-', lw=1.8, alpha=1.)
# Some other model (within bounds)
ptanh = model.pdict(h=0.6731,
omegaM=0.315,
omegaK=0.0,
omegaB=0.045,
w0=-1.,
winf=-0.9,
zc=3,
deltaz=2.5)