def position_dependent_powerspectra(xfrac_dir,
dens_dir,
z,
ps_quantity='signal',
quantity='density',
statistic='mean',
kbins=100,
Ncuts=4):
quantities = ['density', 'xfrac', 'signal']
stats = ['mean', 'skewness', 'kurtosis']
assert quantity in quantities
assert statistic in stats
if ps_quantity == 'signal':
cube = owntools.coeval_21cm(xfrac_dir, dens_dir, z)
elif ps_quantity == 'density':
cube = owntools.coeval_dens(dens_dir, z)
elif ps_quantity == 'xfrac':
cube = owntools.coeval_xfrac(xfrac_dir, z)
stat_functions = [np.mean, scipy.stats.skew, scipy.stats.kurtosis]
P_k_s = np.zeros((kbins, Ncuts**3))
k_s = np.zeros((kbins, Ncuts**3))
stat = np.zeros(Ncuts**3)
stat_func = stat_functions[stats.index(statistic)]
if quantity == 'density':
quant = owntools.coeval_dens(dens_dir, z)
quant = quant / quant.mean(dtype=np.float64) - 1.
elif quantity == 'xfrac':
quant = owntools.coeval_xfrac(xfrac_dir, z)
elif quantity == 'signal':
quant = owntools.coeval_21cm(xfrac_dir, dens_dir, z)
Lx, Ly, Lz = cube.shape[0] / Ncuts, cube.shape[1] / Ncuts, cube.shape[
2] / Ncuts
smaller_cubes = [
cube[i * Lx:(i + 1) * Lx, j * Ly:(j + 1) * Ly, k * Lz:(k + 1) * Lz]
for i in xrange(Ncuts) for j in xrange(Ncuts) for k in xrange(Ncuts)
]
smaller_quant = [
quant[i * Lx:(i + 1) * Lx, j * Ly:(j + 1) * Ly, k * Lz:(k + 1) * Lz]
for i in xrange(Ncuts) for j in xrange(Ncuts) for k in xrange(Ncuts)
]
for i in xrange(len(smaller_cubes)):
pk, ks = c2t.power_spectrum_1d(smaller_cubes[i],
kbins=kbins,
box_dims=c2t.conv.LB / Ncuts)
P_k_s[:, i], k_s[:, i] = pk, ks
stat[i] = stat_func(smaller_quant[i])
return P_k_s, k_s, stat
def position_dependent_cross_correlation_PS(fieldx,
fieldy,
xfrac_dir,
dens_dir,
z,
quantity='density',
kbins=100,
Ncuts=4,
statistic='mean'):
assert fieldx.ndim == fieldy.ndim
stats = ['mean', 'skewness', 'kurtosis']
stat_functions = [np.mean, scipy.stats.skew, scipy.stats.kurtosis]
stat_func = stat_functions[stats.index(statistic)]
R_k_s = np.zeros((kbins, Ncuts**3))
k_s = np.zeros((kbins, Ncuts**3))
stat = np.zeros(Ncuts**3)
if quantity == 'density':
quant = owntools.coeval_dens(dens_dir, z)
quant = quant / quant.mean(dtype=np.float64) - 1.
elif quantity == 'xfrac':
quant = owntools.coeval_xfrac(xfrac_dir, z)
elif quantity == 'signal':
quant = owntools.coeval_21cm(xfrac_dir, dens_dir, z)
Lx, Ly, Lz = np.array(fieldx.shape) / Ncuts
smaller_x = [
fieldx[i * Lx:(i + 1) * Lx, j * Ly:(j + 1) * Ly, k * Lz:(k + 1) * Lz]
for i in xrange(Ncuts) for j in xrange(Ncuts) for k in xrange(Ncuts)
]
smaller_y = [
fieldy[i * Lx:(i + 1) * Lx, j * Ly:(j + 1) * Ly, k * Lz:(k + 1) * Lz]
for i in xrange(Ncuts) for j in xrange(Ncuts) for k in xrange(Ncuts)
]
smaller_quant = [
quant[i * Lx:(i + 1) * Lx, j * Ly:(j + 1) * Ly, k * Lz:(k + 1) * Lz]
for i in xrange(Ncuts) for j in xrange(Ncuts) for k in xrange(Ncuts)
]
for i in xrange(len(smaller_x)):
rk, ks = cross_correlation_power_spectra(smaller_x[i],
smaller_y[i],
kbins=kbins,
box_dims=c2t.conv.LB / Ncuts)
R_k_s[:, i], k_s[:, i] = rk, ks
stat[i] = stat_func(smaller_quant[i])
return R_k_s, k_s, stat
def apply_integrated_bispectrum(xfrac_dir,
dens_dir,
zs,
kbins=100,
Ncuts=4,
field='21cm'):
B_k_s = np.zeros((kbins, len(zs)))
k_s = np.zeros((kbins, len(zs)))
for i in xrange(len(zs)):
if field == '21cm':
cube = owntools.coeval_21cm(xfrac_dir, dens_dir, zs[i])
elif field == 'matter' or field == 'density':
cube = owntools.coeval_dens(dens_dir, zs[i])
cube = cube / cube.mean(dtype=np.float64) - 1.
B_k_s[:, i], k_s[:, i] = integrated_bispectrum_normalized(cube,
Ncuts=Ncuts,
kbins=kbins)
print "Squeezed-limit bispectrum has been calculated for redshift =", zs[
i]
return B_k_s, k_s
Ncuts = 3 xvs_ = 10**np.linspace(np.log10(3.44e-10),np.log10(0.20),10) #xvs = ph_count_info[[np.abs(ph_count_info[:,-2]-x).argmin() for x in xvs_],-2] zs_ = ph_count_info[[np.abs(ph_count_info[:,-2]-x).argmin() for x in xvs_], 0] dens_zs = owntools.get_zs_list(dens_dir, file_type='/*n_all.dat') zs = dens_zs[[np.abs(dens_zs-i).argmin() for i in zs_]] xvs = ph_count_info[[np.abs(ph_count_info[:,0]-i).argmin() for i in zs],-2] i = 9 z = 6.549 cube_21 = owntools.coeval_21cm(xfrac_dir, dens_dir, z, mean_subtract=True) cube_m = owntools.coeval_overdens(dens_dir, z) cube_d = owntools.coeval_dens(dens_dir, z) cube_x = owntools.coeval_xfrac(xfrac_dir, z) P_dd, ks_m = c2t.power_spectrum_1d(cube_m, kbins=100, box_dims=c2t.conv.LB) P_21, ks_x = c2t.power_spectrum_1d(cube_21, kbins=100, box_dims=c2t.conv.LB) f_dd, k_dd = squeezed_bispectrum._integrated_bispectrum_normalized_cross(cube_m, cube_m, Ncuts=Ncuts) f_21d, k_xd = squeezed_bispectrum._integrated_bispectrum_normalized_cross(cube_21, cube_m, Ncuts=Ncuts) f_xx, k_xx = squeezed_bispectrum._integrated_bispectrum_normalized_cross(cube_x-cube_x.mean(), cube_x-cube_x.mean(), Ncuts=Ncuts) f_x_ = squeezed_bispectrum._integrated_bispectrum_normalized_cross1(1-cube_x, cube_m, Ncuts=Ncuts) ks = k_dd.copy() ### Zeta calculation sources = np.loadtxt(parent_dir+'sources/'+str(z)+'-coarser_sources.dat', skiprows=1) M_min = sources[np.nonzero(sources[:,-2]),-2].min()*c2t.conv.M_grid*c2t.const.solar_masses_per_gram #solarunit
quantity = 'density'
if quantity == 'density':
norm = matplotlib.colors.Normalize(vmin=-0.091, vmax=0.114)
label = '$\delta$'
elif quantity == 'xfrac':
norm = matplotlib.colors.Normalize(vmin=0.049, vmax=0.956)
label = '$x_{HII}$'
elif quantity == 'signal':
norm = matplotlib.colors.Normalize(vmin=-5.1, vmax=5.1)
label = '$\delta T_b$'
ii = 1
for z in zs:
field_21 = owntools.coeval_21cm(xfrac_dir, dens_dir, z)
field_d = owntools.coeval_dens(dens_dir, z)
yy, xx, pp = squeezed_bispectrum.position_dependent_cross_correlation_PS(
field_21, field_d, xfrac_dir, dens_dir, z, Ncuts=5, quantity=quantity)
plt.subplot(2, 2, ii)
if quantity == 'signal': pp = pp - pp.mean()
print pp.min(), pp.max()
color_plot_lines(xx, yy, pp, cmap='jet', label=label, norm=norm)
plt.title('$x_v$=' + str(xvs[zs.index(z)]))
plt.ylabel('R$_{\delta,21cm}$')
plt.xlabel('k (Mpc$^{-1}$)')
ii += 1
for z in zs:
cube = owntools.coeval_21cm(xfrac_dir, dens_dir, z)
cube2 = owntools.coeval_overdens(dens_dir, z)
ibc, kc = squeezed_bispectrum.integrated_bispectrum_normalized_cross(