def get_hz_errs(fname):
"""
Load Fisher matrix and invert to get errors on H(z) as a function of z.
"""
root = "output/" + fname
print(root)
# Load cosmo fns.
dat = np.atleast_2d(np.genfromtxt(root + "-cosmofns-zc.dat")).T
zc, Hc, dAc, Dc, fc = dat
z, H, dA, D, f = np.genfromtxt(root + "-cosmofns-smooth.dat").T
kc = np.genfromtxt(root + "-fisher-kc.dat").T
# Load Fisher matrices as fn. of z
Nbins = zc.size
F_list = [
np.genfromtxt(root + "-fisher-full-%d.dat" % i) for i in range(Nbins)
]
# EOS FISHER MATRIX
# Actually, (aperp, apar) are (D_A, H)
pnames = rf.load_param_names(root + "-fisher-full-0.dat")
zfns = [
'bs8',
'fs8',
'H',
'DA',
]
excl = [
'Tb', 'n_s', 'sigma8', 'omegak', 'omegaDE', 'w0', 'wa', 'h', 'gamma',
'N_eff', 'pk*', 'f', 'b_HI', 'gamma0', 'gamma1', 'eta0', 'eta1',
'A_xi', 'logkmg', 'sigma8tot', 'sigma_8', 'k*', 'A', 'aperp', 'apar'
]
F, lbls = rf.combined_fisher_matrix(F_list,
expand=zfns,
names=pnames,
exclude=excl)
print(lbls)
cov = np.linalg.inv(F)
errs = np.sqrt(np.diag(cov))
# Identify functions of z
pH = rf.indices_for_param_names(lbls, 'H*')
errH = 1e2 * errs[pH] / Hc
# Return values
return zc, errH
Nbins = zc.size
F_list = [np.genfromtxt(root+"-fisher-full-%d.dat" % i) for i in range(Nbins)]
# EOS FISHER MATRIX
# Actually, (aperp, apar) are (D_A, H)
pnames = rf.load_param_names(root+"-fisher-full-0.dat")
#zfns = ['A', 'b_HI', 'f', 'H', 'DA', 'aperp', 'apar']
zfns = ['bs8', 'fs8', 'H', 'DA',]
excl = ['Tb', 'n_s', 'sigma8', 'omegak', 'omegaDE', 'w0', 'wa', 'h',
'gamma', 'N_eff', 'pk*', 'f', 'b_HI',
'gamma0', 'gamma1', 'eta0', 'eta1', 'A_xi', 'logkmg',
'sigma8tot', 'sigma_8', 'k*', 'A', 'aperp', 'apar']
F, lbls = rf.combined_fisher_matrix( F_list,
expand=zfns, names=pnames,
exclude=excl )
cov = np.linalg.inv(F)
errs = np.sqrt(np.diag(cov))
# Identify functions of z
pfs8 = rf.indices_for_param_names(lbls, 'fs8*')
err = errs[pfs8] / (cosmo['sigma_8']*fc*Dc)
P.plot( zc, err, color=colours[k], label=labels[k], lw=2.2,
marker=marker[k], markersize=ms[k], markeredgecolor=colours[k],
dashes=linestyle[k] )
# Load actual f.sigma_8 data and plot it
dat = np.genfromtxt("fsigma8_data.dat").T
#P.plot(dat[1], dat[3], 'kD')
np.genfromtxt(root + "-fisher-full-%d.dat" % i) for i in range(Nbins)
]
#F_list = [np.genfromtxt(root+"-fisher-full-%d.dat" % i) for i in np.where(zc>0.5)[0]]
#Nbins = np.where(zc > 0.5)[0].size
#if Nbins == 0: continue
# EOS FISHER MATRIX
pnames = rf.load_param_names(root + "-fisher-full-0.dat")
zfns = [
'b_HI',
]
excl = [
'Tb', 'f', 'aperp', 'apar', 'DA', 'H', 'pk*', 'fs8', 'bs8', 'N_eff'
]
F, lbls = rf.combined_fisher_matrix(F_list,
expand=zfns,
names=pnames,
exclude=excl)
# Add Planck prior
print "*** Using DETF Planck prior ***"
l2 = ['n_s', 'w0', 'wa', 'omega_b', 'omegak', 'omegaDE', 'h', 'sigma8']
F_detf = euclid.detf_to_rf("DETF_PLANCK_FISHER.txt", cosmo, omegab=False)
Fpl, lbls = rf.add_fisher_matrices(F, F_detf, lbls, l2, expand=True)
# Remove extra params
Fpl, lbls = rf.combined_fisher_matrix([
Fpl,
],
expand=[],
names=lbls,
exclude=[
# Load Fisher matrices as fn. of z
Nbins = zc.size
F_list = [
np.genfromtxt(root + "-fisher-full-%d.dat" % i) for i in range(Nbins)
]
# EOS FISHER MATRIX
pnames = rf.load_param_names(root + "-fisher-full-0.dat")
zfns = [
'b_HI',
]
#zfns = ['bs8',]
excl = exclude_all + mgexclude[MGMODE]
F, lbls = rf.combined_fisher_matrix(F_list,
expand=zfns,
names=pnames,
exclude=excl)
# Relabel galaxy bias from low-z survey and sum current survey + low-z
#if 'BOSS' not in labels[k]:
# F, lbls = rf.add_fisher_matrices(F_lowz, F, lbls_lowz, lbls, expand=True)
#print lbls
# Add Planck prior
print "*** Using DETF Planck prior ***"
l2 = ['n_s', 'w0', 'wa', 'omega_b', 'omegak', 'omegaDE', 'h', 'sigma_8']
F_detf = euclid.detf_to_rf("DETF_PLANCK_FISHER.txt", cosmo, omegab=False)
Fpl, lbls = rf.add_fisher_matrices(F, F_detf, lbls, l2, expand=True)
# Decide whether to fix various parameters
fixed_params = []
F_list = [
np.genfromtxt(root + "-fisher-full-%d.dat" % i) for i in range(Nbins)
]
# EOS FISHER MATRIX
# Actually, (aperp, apar) are (D_A, H)
pnames = [
'A', 'b_HI', 'Tb', 'sigma_NL', 'sigma8', 'n_s', 'f', 'aperp', 'apar',
'omegak', 'omegaDE', 'w0', 'wa', 'h', 'gamma', 'fNL'
]
pnames += ["pk%d" % i for i in range(kc.size)]
zfns = [
1,
]
F, lbls = rf.combined_fisher_matrix(F_list,
expand=zfns,
names=pnames,
exclude=[2, 4, 5, 6, 7, 8])
# Just do the simplest thing for P(k)
cov = np.sqrt(1. / np.diag(F)[-kc.size:])
cov[np.where(np.isinf(cov))] = 1e4
#pk = cosmo['pk_nobao'](kc) * (1. + fbao(kc))
P.plot(kc, cov, lw=1.5, label=labels[k])
P.legend(loc='upper left', prop={'size': 'large'})
P.ylim((1e-3, 400.))
P.xlim((3e-3, 1e0))
P.xscale('log')
P.yscale('log')
P.axvline(2.3e-2, ls='dotted', color='k')
P.axvline(3.7e-1, ls='dotted', color='k')
# EOS FISHER MATRIX
# Actually, (aperp, apar) are (D_A, H)
pnames = [
'A', 'b_HI', 'Tb', 'sigma_NL', 'sigma8', 'n_s', 'f', 'aperp',
'apar', 'omegak', 'omegaDE', 'w0', 'wa', 'h', 'gamma'
]
pnames += ["pk%d" % i for i in range(kc.size)]
zfns = [
1,
]
excl = [2, 6, 7, 8, 14] # omega_k free 4,5
excl += [i for i in range(len(pnames)) if "pk" in pnames[i]]
F, lbls = rf.combined_fisher_matrix(F_list[:l],
expand=zfns,
names=pnames,
exclude=excl)
# Add Planck prior
#Fpl = euclid.add_detf_planck_prior(F, lbls, info=False)
#Fpl = euclid.add_planck_prior(F, lbls, info=False)
if USE_DETF_PLANCK_PRIOR:
# DETF Planck prior
print "*** Using DETF Planck prior ***"
l2 = ['n_s', 'w0', 'wa', 'omega_b', 'omegak', 'omegaDE', 'h']
F_detf = euclid.detf_to_rf("DETF_PLANCK_FISHER.txt", cosmo)
Fpl, lbls = rf.add_fisher_matrices(F,
F_detf,
lbls,
l2,
expand=True)
else:
Nbins = zc.size
# EOS FISHER MATRIX
pnames = [
'A', 'b_HI', 'Tb', 'sigma_NL', 'sigma8', 'n_s', 'f', 'aperp', 'apar',
'omegak', 'omegaDE', 'w0', 'wa', 'h', 'gamma'
]
#0:A, 1:b_HI, 2:sigma_NL, [3:omegak,] 4:omegaDE, 5:w0, 6:wa, 7:h, 8:gamma
zfns = [
1,
]
# Marginalise over omega_k
F_eos, lbls = rf.combined_fisher_matrix(
F_eos_list,
#exclude=[2,4,5,6,7,8,9,12],
exclude=[2, 4, 5, 6, 7, 8, 9, 14],
expand=zfns,
names=pnames)
Finv = np.linalg.inv(F_eos) # Pre-invert, for efficiency
print "Cond. No.:", np.linalg.cond(F_eos)
print "sig(h):", np.sqrt(Finv[6, 6])
px = rf.indexes_for_sampled_fns(4, zc.size, zfns) # x 4
py = rf.indexes_for_sampled_fns(5, zc.size, zfns) # y 5
#rf.plot_corrmat(Finv, lbls)
"""
# Fix omega_k
F_eos2, lbls2 = rf.combined_fisher_matrix( F_eos_list,
exclude=[2,4,5,6,7,8, 9,12],
Nbins = zc.size
F_list = [
np.genfromtxt(root + "-fisher-full-%d.dat" % i) for i in range(Nbins)
]
# EOS FISHER MATRIX
pnames = rf.load_param_names(root + "-fisher-full-0.dat")
zfns = [
'b_HI',
]
excl = [
'Tb', 'f', 'fs8', 'bs8', 'aperp', 'apar', 'DA', 'H', 'gamma', 'N_eff',
'pk*'
]
F, lbls = rf.combined_fisher_matrix(F_list,
expand=zfns,
names=pnames,
exclude=excl)
# Add Planck prior
#Fpl = euclid.add_detf_planck_prior(F, lbls, info=False)
#Fpl = euclid.add_planck_prior(F, lbls, info=False)
# (a) DETF Planck prior
#print "*** Using DETF Planck prior ***"
#l2 = ['n_s', 'w0', 'wa', 'omega_b', 'omegak', 'omegaDE', 'h', 'sigma8']
#F_detf = euclid.detf_to_rf("DETF_PLANCK_FISHER.txt", cosmo, omegab=False)
#Fpl, lbls = rf.add_fisher_matrices(F, F_detf, lbls, l2, expand=True)
# (b) Planck Fisher matrix (derived from Planck 2015 MCMC chains)
lbls_planck = ['n_s', 'Omega_b', 'omegaDE', 'h', 'sigma8', 'Mnu', 'Neff']
F_planck = np.genfromtxt(
"/home/phil/oslo/Dropbox/HI_neutrinos/fisher_code/planck/planck_derived_fisher.dat"
pnames += ["pk%d" % i for i in range(kc.size)]
zfns = [
1,
]
excl = [
2,
6,
7,
8,
14,
]
excl += [i for i in range(len(pnames)) if "pk" in pnames[i]]
F, lbls = rf.combined_fisher_matrix(F_list,
expand=zfns,
names=pnames,
exclude=excl)
# Apply Planck prior
if USE_DETF_PLANCK_PRIOR:
# DETF Planck prior
print "*** Using DETF Planck prior ***"
l2 = ['n_s', 'w0', 'wa', 'omega_b', 'omegak', 'omegaDE', 'h', 'sigma8']
F_detf = euclid.detf_to_rf("DETF_PLANCK_FISHER.txt",
cosmo,
omegab=False)
Fpl, lbls = rf.add_fisher_matrices(F, F_detf, lbls, l2, expand=True)
else:
# Euclid Planck prior
print "*** Using Euclid (Mukherjee) Planck prior ***"
l2 = ['n_s', 'w0', 'wa', 'omega_b', 'omegak', 'omegaDE', 'h']
Fe = euclid.planck_prior_full
