def cross_correlation_power_spectra(fielda, fieldb, kbins=100, box_dims=None):
Pab, kab = c2t.cross_power_spectrum_1d(fielda,
fieldb,
kbins=kbins,
box_dims=box_dims)
Paa, kaa = c2t.power_spectrum_1d(fielda, kbins=kbins, box_dims=box_dims)
Pbb, kbb = c2t.power_spectrum_1d(fieldb, kbins=kbins, box_dims=box_dims)
Rxx = Pab / np.sqrt(Paa * Pbb)
return Rxx, kab
def _integrated_bispectrum_normalized_cross(cube,
cube2,
Ncuts=4,
kbins=20,
box_dims=None):
#assert statistic in ['mean']
assert cube.shape == cube2.shape
assert cube.shape[0] % Ncuts == 0 and cube.shape[
1] % Ncuts == 0 and cube.shape[2] % Ncuts == 0
Lx, Ly, Lz = cube.shape[0] / Ncuts, cube.shape[1] / Ncuts, cube.shape[
2] / Ncuts
rLs = [[Lx / 2. + i * Lx, Ly / 2. + j * Ly, Lz / 2. + k * Lz]
for i in xrange(Ncuts) for j in xrange(Ncuts)
for k in xrange(Ncuts)]
B_k = np.zeros(kbins, dtype=np.float64)
P_k = np.zeros(kbins, dtype=np.float64)
sig2 = 0
n_box = Ncuts**3
V_L = (Lx * Ly * Lz)
for i in xrange(n_box):
w = W_L(cube, rLs[i], [Lx, Ly, Lz])
w2 = W_L(cube2, rLs[i], [Lx, Ly, Lz])
c = cube * w
c2 = cube2 * w2
pk, ks = c2t.power_spectrum_1d(c, kbins=kbins, box_dims=box_dims)
d_mean = c2.sum(dtype=np.float64) / V_L
B_k += pk * d_mean
P_k += pk
sig2 += (d_mean)**2 #c2.var(dtype=np.float64)
print 100 * (i + 1) / n_box, "%"
B_k = B_k / n_box
P_k = P_k / n_box
sig2 = sig2 / n_box
return B_k / P_k / sig2, ks
def integrated_bispectrum_normalized_cross(cube,
cube2,
Ncuts=4,
statistic='mean',
kbins=100):
assert statistic in ['mean']
assert cube.shape[0] % Ncuts == 0 and cube.shape[
1] % Ncuts == 0 and cube.shape[2] % Ncuts == 0
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_cubes2 = [
cube2[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)
]
B_k = np.zeros(kbins, dtype=np.float64)
P_k = np.zeros(kbins, dtype=np.float64)
sig2 = 0
for i in xrange(Ncuts**3):
c = smaller_cubes[i]
c2 = smaller_cubes2[i]
pk, ks = c2t.power_spectrum_1d(c,
kbins=kbins,
box_dims=c2t.conv.LB / Ncuts)
B_k += pk * c2.mean(dtype=np.float64)
P_k += pk
sig2 += c2.mean(dtype=np.float64)**2 #c2.var(dtype=np.float64)
B_k = B_k / Ncuts**3
P_k = P_k / Ncuts**3
sig2 = sig2 / Ncuts**3
return B_k / P_k / sig2, ks
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 integrated_bispectrum(cube, Ncuts=4, statistic='mean', kbins=100):
assert statistic in ['mean']
assert cube.shape[0] % Ncuts == 0 and cube.shape[
1] % Ncuts == 0 and cube.shape[2] % Ncuts == 0
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)
]
B_k = np.zeros(kbins)
for c in smaller_cubes:
bk, ks = c2t.power_spectrum_1d(c,
kbins=kbins,
box_dims=c2t.conv.LB / Ncuts)
B_k += bk * c.mean(dtype=np.float64)
B_k = B_k / Ncuts**3
return B_k, ks
base_path = '/disk/sn-12/garrelt/Science/Simulations/Reionization/C2Ray_WMAP5/114Mpc_WMAP5'
density_filename = base_path+'/coarser_densities/halos_removed/30.000n_all.dat'
velocity_filename = base_path+'/coarser_densities/halos_removed/30.000v_all.dat'
#Enable output
c2t.set_verbose(True)
#We are using the 114/h Mpc simulation box, so set all the proper conversion factors
c2t.set_sim_constants(boxsize_cMpc = 114.)
#Read density
dfile = c2t.DensityFile(density_filename)
#Read a velocity data file
vfile = c2t.VelocityFile(velocity_filename)
kms = vfile.get_kms_from_density(dfile)
#Make a distorted box. Assume x_i = 0. We could of course also have calculated dT like in example.py
#and passed that to get_distorted_dt
distorted = c2t.get_distorted_dt(dfile.raw_density.astype('float64'), kms, dfile.z, los_axis=0, num_particles=20)
#Calculate power spectra
ps_dist,k = c2t.power_spectrum_1d(distorted)
ps_nodist,k = c2t.power_spectrum_1d(dfile.raw_density)
#Plot ratio
pl.semilogx(k, ps_dist/ps_nodist)
pl.xlabel('$k \; \mathrm{[Mpc^{-1}]}$')
pl.ylabel('$P_k^{\mathrm{PV}}/P_k^{\mathrm{NoPV}}$')
pl.show()
#Read density
dfile = c2t.DensityFile(density_filename)
#Read velocity data file and get the actual velocity
vfile = c2t.VelocityFile(velocity_filename)
kms = vfile.get_kms_from_density(dfile)
#To speed things up, we will assume that the IGM is completely neutral, so instead
#of reading an ionization fraction file, we will just make an array of zeros
xi = np.zeros_like(dfile.cgs_density)
#Calculate the dT
dT_realspace = c2t.calc_dt(xi, dfile, z=dfile.z)
#Make the redshift-space volume
dT_redshiftspace = c2t.get_distorted_dt(dT_realspace, kms, \
dfile.z, los_axis=0, num_particles=20)
#Calculate spherically-averaged power spectra,
#using 20 logarithmically spaced k bins, from 1e-1 to 10
kbins = 10**np.linspace(-1, 1, 20)
ps_dist, k = c2t.power_spectrum_1d(dT_redshiftspace, kbins)
ps_nodist, k = c2t.power_spectrum_1d(dT_realspace, kbins)
#Plot ratio. On large scales, this should be close to 1.83
pl.semilogx(k, ps_dist/ps_nodist)
pl.xlabel('$k \; \mathrm{[Mpc^{-1}]}$')
pl.ylabel('$P_k^{\mathrm{PV}}/P_k^{\mathrm{NoPV}}$')
pl.show()
#Enable output
c2t.set_verbose(True)
#We are using the 114/h Mpc simulation box, so set all the proper conversion factors
c2t.set_sim_constants(boxsize_cMpc=114.)
#Read density
dfile = c2t.DensityFile(density_filename)
#Read a velocity data file
vfile = c2t.VelocityFile(velocity_filename)
kms = vfile.get_kms_from_density(dfile)
#Make a distorted box. Assume x_i = 0. We could of course also have calculated dT like in example.py
#and passed that to get_distorted_dt
distorted = c2t.get_distorted_dt(dfile.raw_density.astype('float64'),
kms,
dfile.z,
los_axis=0,
num_particles=20)
#Calculate power spectra
ps_dist, k = c2t.power_spectrum_1d(distorted)
ps_nodist, k = c2t.power_spectrum_1d(dfile.raw_density)
#Plot ratio
pl.semilogx(k, ps_dist / ps_nodist)
pl.xlabel('$k \; \mathrm{[Mpc^{-1}]}$')
pl.ylabel('$P_k^{\mathrm{PV}}/P_k^{\mathrm{NoPV}}$')
pl.show()
#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 M_max = sources[np.nonzero(sources[:,-2]),-2].max()*c2t.conv.M_grid*c2t.const.solar_masses_per_gram #solarunit M_halo_sum = sources[:,-2].sum()*c2t.conv.M_grid*c2t.const.solar_masses_per_gram #solarunit mpc_to_cm = 3.086e24
z_arr = np.loadtxt(z_filename, unpack=True)
c2t.set_sim_constants(boxsize_cMpc = 244.)
for i in range(1,len(z_arr)):
output_filename = output_path+'%.3f.dat'%(z_arr[i])
out1_filename = out1_path+'%.3f.dat'%(z_arr[i])
print 'z = %.3f'% (z_arr[i])
dT_file = ''.join(glob.glob('./dT_boxes/dT_%.3f.cbin'%(z_arr[i])))
print 'dT file = %s' % dT_file
dT_box = c2t.read_cbin(dT_file, bits=64, order='F')
dT_rsd_file = ''.join(glob.glob('./dT_pv_boxes/dT_pv_%.3f.cbin'%(z_arr[i])))
print 'dT_pv file = %s' % dT_rsd_file
dT_rsd_box = c2t.read_cbin(dT_rsd_file, bits=64, order='F')
print 'Calculating power spectra...'
ps_raw,k = c2t.power_spectrum_1d(dT_box, kbins=75)
ps_rsd,k = c2t.power_spectrum_1d(dT_rsd_box, kbins=75)
out = open(output_filename, 'w')
out1 = open(out1_filename, 'w')
for j in range(0,len(k)):
out.write('%.3f %.4f\n' % (k[j],ps_raw[j]))
out1.write('%.3f %.4f\n' % (k[j],ps_rsd[j]))
out.close()
out1.close()
density_file = ''.join(glob.glob(density_path+'%.3fn_all.dat'%(z_arr[i])))
if not density_file:
zi = np.where(z_f==z_arr[i])
density_file = ''.join(glob.glob(density_path+'%.3fn_all.dat'%(z_f[zi[0]-1])))
print 'Density file = %s' % density_file
dfile = c2t.DensityFile(density_file)
xfrac_file = ''.join(glob.glob(xfrac_path+'xfrac3d_%.3f.bin'%(z_arr[i])))
print 'Ionized fraction file = %s' % xfrac_file
xfile = c2t.XfracFile(xfrac_file)
dT_file = ''.join(glob.glob('./dT_boxes/dT_%.3f.cbin'%(z_arr[i])))
print 'dT file = %s' % dT_file
dT_box = c2t.read_cbin(dT_file, bits=64, order='F')
ps_dT,kT = c2t.power_spectrum_1d(dT_box, kbins=75)
ps_xi,kxi = c2t.power_spectrum_1d(xfile.xi/xfile.xi.mean()-1, kbins=75)
ps_x,kx = c2t.power_spectrum_1d((1-xfile.xi)/(1-xfile.xi.mean())-1,kbins=75)
ps_d,kd = c2t.power_spectrum_1d(dfile.raw_density/dfile.raw_density.mean()-1, kbins=75)
ps_xid,kxid = c2t.cross_power_spectrum_1d((1-xfile.xi)/(1-xfile.xi.mean())-1,(dfile.raw_density/dfile.raw_density.mean()-1), kbins=75)
if( kT.all() != kxi.all() ): print 'poop'
if( kT.all() != kd.all() ): print 'poop2'
factor = pow(dT_box.mean(),2)
out = open(output_filename, 'w')
for j in range(len(kT)):
out.write('%.3f %.4f %.4f %.4f %.4f %.4f\n' % (kT[j],ps_dT[j],ps_xi[j]*factor,ps_x[j]*factor,ps_d[j]*factor,ps_xid[j]*factor))
out.close()
#Read density
dfile = c2t.DensityFile(density_filename)
#Read velocity data file and get the actual velocity
vfile = c2t.VelocityFile(velocity_filename)
kms = vfile.get_kms_from_density(dfile)
#To speed things up, we will assume that the IGM is completely neutral, so instead
#of reading an ionization fraction file, we will just make an array of zeros
xi = np.zeros_like(dfile.cgs_density)
#Calculate the dT
dT_realspace = c2t.calc_dt(xi, dfile, z=dfile.z)
#Make the redshift-space volume
dT_redshiftspace = c2t.get_distorted_dt(dT_realspace, kms, \
dfile.z, los_axis=0, num_particles=20)
#Calculate spherically-averaged power spectra,
#using 20 logarithmically spaced k bins, from 1e-1 to 10
kbins = 10**np.linspace(-1, 1, 20)
ps_dist, k = c2t.power_spectrum_1d(dT_redshiftspace, kbins)
ps_nodist, k = c2t.power_spectrum_1d(dT_realspace, kbins)
#Plot ratio. On large scales, this should be close to 1.83
pl.semilogx(k, ps_dist / ps_nodist)
pl.xlabel('$k \; \mathrm{[Mpc^{-1}]}$')
pl.ylabel('$P_k^{\mathrm{PV}}/P_k^{\mathrm{NoPV}}$')
pl.show()
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]
P_k_m = []
P_k_x = []
## Global Power Spectrum
for i in xrange(len(zs)):
z = zs[i]
print z
cube_x = owntools.coeval_21cm(xfrac_dir, dens_dir, z, mean_subtract=True)
cube_m = owntools.coeval_overdens(dens_dir, z)
pk_m, ks_m = c2t.power_spectrum_1d(cube_m, kbins=100, box_dims=c2t.conv.LB)
pk_x, ks_x = c2t.power_spectrum_1d(cube_x, kbins=100, box_dims=c2t.conv.LB)
P_k_m.append((pk_m, ks_m))
P_k_x.append((pk_x, ks_x))
iB_k_mm = []
iB_k_xm = []
### Response Functions
for i in xrange(len(zs)):
z = zs[i]
print z
cube_x = owntools.coeval_21cm(xfrac_dir, dens_dir, z, mean_subtract=True)
cube_m = owntools.coeval_overdens(dens_dir, z)
ibk_m_m, k_m_m = squeezed_bispectrum._integrated_bispectrum_normalized_cross(
cube_m, cube_m, Ncuts=Ncuts)
ibk_x_m, k_x_m = squeezed_bispectrum._integrated_bispectrum_normalized_cross(
c2t.set_sim_constants(500)
#zs = [20.134, 17.848, 15.596, 12.603, 10.11, 9.026]
zs = [20.134, 12.603, 9.026, 7.305, 6.549, 6.113]
colors = ['b', 'r', 'g', 'k', 'c', 'm']
xfrac_dir = '/disk/dawn-1/garrelt/Reionization/C2Ray_WMAP7/500Mpc/500Mpc_f2_0_300/results/'
dens_dir = '/disk/dawn-1/garrelt/Reionization/C2Ray_WMAP7/500Mpc/coarser_densities/nc300/'
plt.figure()
for i in xrange(len(zs)):
z = zs[i]
cube_x = owntools.coeval_xfrac(xfrac_dir, z)
cube_x = cube_x - cube_x.mean()
cube_m = owntools.coeval_overdens(dens_dir, z)
P_mm, k_mm = c2t.power_spectrum_1d(cube_m, kbins=100)
P_xx, k_xx = c2t.power_spectrum_1d(cube_x, kbins=100)
P_mx, k_mx = c2t.cross_power_spectrum_1d(cube_x, cube_m, kbins=100)
plt.subplot(2, 3, i + 1)
plt.title('z=' + str(z))
plt.loglog(k_mm, P_mm, c='b')
plt.loglog(k_xx, P_xx, c='r')
plt.loglog(k_mx, P_mx, c='g')
print 'z=', z, 'done'
plt.suptitle('BLUE: $\delta \delta$, RED: $x_n x_n$, GREEN: $\delta x_n$')
for i in xrange(len(zs)):
plt.subplot(2, 3, i + 1)
plt.ylim(2e-4, 2e4)
plt.xlabel('k')
plt.ylabel('P(k)')
