Пример #1
0
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()
Пример #3
0
 """
 # 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, 
Пример #4
0
    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],
Пример #5
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'})
Пример #6
0
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)