y = 1.
alpha = [1.52, 2.48, 3.44]
cmap = matplotlib.cm.get_cmap("spectral")
ZMAX = 1.39
print "Max:", np.max(zc)
ax = P.subplot(111)
Nused = np.where(zc <= ZMAX)[0].size # No. of z bins that are actually used
zvals = []
for i in range(len(p1)):
#if i % 2 == 0: continue
if zc[i] > ZMAX: continue
zvals.append(zc[i])
a, b, ang = rf.ellipse_for_fisher_params(p1[i], p2[i], F_b)
a /= rf.bias_HI(zc[i], cosmo)
b /= fc[i]
c = i * 0.97 / float(Nused - 1) # Colour (must be 0 <= c <= 1)
print c
# Get 1,2,3-sigma ellipses and plot
ellipses = [
matplotlib.patches.Ellipse(xy=(x, y),
width=alpha[k] * b,
height=alpha[k] * a,
angle=ang,
fc='none',
ec=cmap(c),
lw=1.5) for k in [
0,
]
print "OMEGA_HI", omegaHI[0]
p_omegaHI = np.polyfit(zz, omegaHI, deg=2)
print "B_HI", bHI[0]
p_bHI = np.polyfit(zz, bHI, deg=2)
print "T_B", Tb[0]
p_Tb = np.polyfit(zz, Tb, deg=2)
# Omega_HI
P.subplot(311)
P.plot(z, rf.omega_HI(z, e.cosmo), 'r-', lw=1.5)
P.plot(zz, omegaHI, 'bo')
P.plot(zz, poly(p_omegaHI[::-1], zz), 'g-')
P.ylabel("$\Omega_{HI}(z)$")
# b_HI
P.subplot(312)
P.plot(z, rf.bias_HI(z, e.cosmo), 'r-', lw=1.5)
P.plot(zz, bHI, 'bo')
P.plot(zz, poly(p_bHI[::-1], zz), 'g-')
P.ylabel("$b_{HI}(z)$")
# T_b
P.subplot(313)
P.plot(z, rf.Tb(z, e.cosmo, formula='powerlaw'), 'r-', lw=1.5)
P.plot(z, rf.Tb(z, e.cosmo, formula='santos'), 'b-', lw=1.5)
P.plot(zz, poly(p_Tb[::-1], zz), 'g-')
P.plot(zz, Tb, 'bo')
P.ylabel("$T_b(z)$")
P.show()
"""
# FIXME: Disable to get redshift markers
#marker[k] = None
print lbls2
print errs2
# Load bias for experiment
try:
print names[k]
expt = rf.experiments_galaxy.load_expt(expts[k])
b = expt['b']
print b
except:
print "IM survey!"
b = rf.bias_HI(zc, e.cosmo)
pass
bs8 = cosmo['sigma_8']*b*Dc
# (1) Plot bs8 errors as fn. of redshift (b_1 fixed)
err2 = errs2[pbs8_2]
print err2/bs8
line = ax[2].plot( zc, err2/bs8, color=colours[k], label=labels[k],
lw=2.4, ls='solid', marker='None', markersize=ms[k],
markeredgecolor=colours[k] )
ax[2].set_ylabel('$\sigma(b \sigma_8) / (b \sigma_8)$', labelpad=15.,
fontdict={'fontsize':'xx-large'})
# (2) Plot fs8 errors as fn. of redshift (b_1 fixed)
#err2 = errs2[pfs8_2] / (cosmo['sigma_8']*fc*Dc)
#line = ax[1].plot( zc, err2, color=colours[k], label=labels[k], lw=1.8,
for jj in pf:
print jj, lbls[jj]
print "-" * 50
print names[k]
print "-" * 50
# Invert matrix
cov_pl = np.linalg.inv(F)
for jj in range(pf.size):
#if jj % 2 == 0: continue
print jj, lbls[pb[jj]], lbls[pf[jj]]
x = rf.bias_HI(zc[jj], cosmo) * cosmo['sigma_8']
y = fc[jj] * cosmo['sigma_8']
# Plot contours for w0, wa; omega_k free
w, h, ang, alpha = rf.ellipse_for_fisher_params(pb[jj],
pf[jj],
None,
Finv=cov_pl)
transp = [1., 0.85]
ellipses = [
matplotlib.patches.Ellipse(xy=(x, y),
width=alpha[kk] * w,
height=alpha[kk] * h,
angle=ang,
fc=colours[k][kk],
ec=colours[k][0],
for j in range(len(fNL)):
zc = np.load("Xnongauss-fnl-zc-%d.npy"%j)
sigmas.append(np.load("tmp/Xnongauss-fnl-%d.npy" % j))
sigmas_bz.append(np.load("tmp/nongauss-fnl-zbias-%d.npy" % j))
sigma_fNL.append(sigmas[j][-zc.size:])
sigma_fNL_bz.append(sigmas_bz[j][-zc.size:])
sigma_bias.append(sigmas_bz[j][1:zc.size+1])
col = ['r', 'b', 'y']
name = ["f_NL = " + str(int(_fNL)) for _fNL in fNL]
# Get bias as fn. of z
cosmo = rf.experiments.cosmo
cosmo['bHI0'] = 0.702
bias = rf.bias_HI(zc, cosmo)
P.subplot(111)
for i in range(len(fNL)):
#P.errorbar(zc, fNL[i]*np.ones(zc.size), yerr=sigma_fNL[i], marker='.', color=col[i], lw=1.5, label=name[i])
P.plot(zc, sigma_fNL[i], lw=1.5, color=col[i], marker='.', label=name[i])
#P.plot(zc, sigma_fNL_bz[i], lw=1.5, color=col[i], marker='.', ls='dashed')
#P.plot(zc, bias - 1., 'k-')
#P.axhline(0., ls='dotted', color='k')
#P.xlim((np.min(zs), np.max(zs)))
P.ylim((0., 150.))
P.xlim((0., 3.6))
# Display options
P.legend(loc='upper right', prop={'size':'x-large'})
import pylab as P
from rfwrapper import rf
from radiofisher.experiments import cosmo
def rho_c(z):
"""
Critical density, in units of Msun Mpc^-2
"""
E2 = cosmo['omega_M_0'] * (1. + z)**3. + cosmo['omega_lambda_0']
return 2.76e7 * (100. * cosmo['h'])**2. * E2
z = np.linspace(-0.1, 5., 1000)
oHI = rf.omega_HI(z, cosmo)
bHI = rf.bias_HI(z, cosmo)
# Load Prochaska & Wolfe data (10^8 Msun Mpc^-3)
pw_zmin, pw_zmax, pw_rhoHI, pw_errp, pw_errm = np.genfromtxt(
"HI_evolution/prochaska_wolfe_rhoHI.dat").T
pw_zc = 0.5 * (pw_zmin + pw_zmax)
pw_dz = 0.5 * (pw_zmax - pw_zmin)
pw_omegaHI = pw_rhoHI * 1e8 / rho_c(0.) / 1e-3
pw_err_omegaHI_p = pw_errp * 1e8 / rho_c(0.) / 1e-3
pw_err_omegaHI_m = pw_errm * 1e8 / rho_c(0.) / 1e-3
# Load Noterdaeme et al. data (omega_HI / 10^-3)
n_zmin, n_zmax, n_omegaHI, n_err = np.genfromtxt(
"HI_evolution/noterdaeme_omegaHI.dat").T
n_zc = 0.5 * (n_zmin + n_zmax)
n_dz = 0.5 * (n_zmax - n_zmin)