def find_CF(snapshot, snapnum, grid, MAS, do_RSD, axis, threads, ptype, fcf,
save_multipoles):
if os.path.exists(fcf): return 0
# read header
head = readgadget.header(snapshot)
BoxSize = head.boxsize / 1e3 #Mpc/h
Nall = head.nall #Total number of particles
Masses = head.massarr * 1e10 #Masses of the particles in Msun/h
Omega_m = head.omega_m
Omega_l = head.omega_l
redshift = head.redshift
Hubble = 100.0 * np.sqrt(Omega_m *
(1.0 + redshift)**3 + Omega_l) #km/s/(Mpc/h)
h = head.hubble
# read snapshot
pos = readgadget.read_block(snapshot, "POS ", ptype) / 1e3 #Mpc/h
# move particles to redshift-space
if do_RSD:
vel = readgadget.read_block(snapshot, "VEL ", ptype) #km/s
RSL.pos_redshift_space(pos, vel, BoxSize, Hubble, redshift, axis)
# calculate CF
delta = np.zeros((grid, grid, grid), dtype=np.float32)
if len(ptype) > 1: #for multiple particles read masses
mass = np.zeros(pos.shape[0], dtype=np.float32)
offset = 0
for j in ptype:
mass[offset:offset + Nall[j]] = Masses[j]
offset += Nall[j]
MASL.MA(pos, delta, BoxSize, MAS, W=mass)
else:
MASL.MA(pos, delta, BoxSize, MAS)
delta /= np.mean(delta, dtype=np.float64)
delta -= 1.0
CF = PKL.Xi(delta, BoxSize, MAS, axis, threads)
# save results to file
if save_multipoles:
np.savetxt(fcf,
np.transpose(
[CF.r3D, CF.xi[:, 0], CF.xi[:, 1], CF.xi[:, 2]]),
delimiter='\t')
else:
np.savetxt(fcf, np.transpose([CF.r3D, CF.xi[:, 0]]), delimiter='\t')
import Pk_library as PKL
import numpy as np
import h5py
import sys
test_cube = np.load('../dat/processed/test_cube_target.npy')
pred_cube = np.load('../dat/processed/test_cube_final_prediction.npy')
benchmark_cube = np.load('../dat/processed/benchmark_cube.npy')
BoxSize = 31.82373046875 #Size of the density field in Mpc/h
MAS = None
threads = 16
axis = 0
# compute the correlation function
CF = PKL.Xi(test_cube, BoxSize, MAS, axis, threads)
r = CF.r3D #radii in Mpc/h
xi0 = CF.xi[:, 0] #correlation function (monopole)
#xi2 = CF.xi[:,1] #correlation function (quadrupole)
#xi4 = CF.xi[:,2] #correlation function (hexadecapole)
# save correlation function and r
np.save('../processed/target_r_values.npy', r)
np.save('../processed/target_xi0_values.npy', xi0)
# compute dm2gal corr func
CF = PKL.Xi(pred_cube, BoxSize, MAS, axis, threads)
r = CF.r3D #radii in Mpc/h
xi0 = CF.xi[:, 0] #correlation function (monopole)
#xi2 = CF.xi[:,1] #correlation function (quadrupole)
#xi4 = CF.xi[:,2] #correlation function (hexadecapole)
print '\nREADING SNAPSHOTS PROPERTIES'
head = readsnap.snapshot_header(snapshot_fname)
BoxSize = head.boxsize / 1e3 #Mpc/h
Nall = head.nall
Masses = head.massarr * 1e10 #Msun/h
Omega_m = head.omega_m
Omega_l = head.omega_l
redshift = head.redshift
Hubble = 100.0 * np.sqrt(Omega_m *
(1.0 + redshift)**3 + Omega_l) #km/s/(Mpc/h)
h = head.hubble
fout = 'CF_CDM_z=%.3f.txt' % redshift
# read the positions and masses of the CDM particles
pos = readsnap.read_block(snapshot_fname, "POS ", parttype=1) / 1e3 #Mpc/h
# compute delta_CDM
delta = np.zeros((dims, dims, dims), dtype=np.float32)
MASL.MA(pos, delta, BoxSize, MAS)
print '%.6e should be equal to\n%.6e'\
%(np.sum(delta,dtype=np.float64),len(pos))
delta /= np.mean(delta, dtype=np.float64)
delta -= 1.0
#compute the correlation function
CF = PKL.Xi(delta, BoxSize, MAS, threads=8)
#save results to file
np.savetxt(fout, np.transpose([CF.r3D, CF.xi]))
# do the lognormal transformation
if do_lognormal:
sigma_G = np.sqrt(np.mean(delta_r**2, dtype=np.float64))
print('sigma_G =', sigma_G)
delta_r = np.exp(delta_r - sigma_G**2 / 2.0) - 1.0
print('<delta_NL(r)> =', np.mean(delta_r, dtype=np.float64))
print(np.min(delta_r), '< delta_NL(r) <', np.max(delta_r))
# save density and velocity field to file
np.save(fout_df, [delta_r, Vx_r, Vy_r, Vz_r])
############ Final checks ###########
# Compute power spectrum & correlation function of the density field
Pk = PKL.Pk(delta_r, BoxSize, axis, MAS, threads)
CF = PKL.Xi(delta_r, BoxSize, MAS, axis, threads)
np.savetxt(f_Pk, np.transpose([Pk.k3D, Pk.Pk[:, 0]]))
np.savetxt(f_CF, np.transpose([CF.r3D, CF.xi[:, 0]]))
del delta_r
"""
#compute the power spectrum of the theta=i\grad V
Vx_r = np.reshape(Vx_r,(dims,dims,dims))
Vy_r = np.reshape(Vy_r,(dims,dims,dims))
Vz_r = np.reshape(Vz_r,(dims,dims,dims))
Vx_k = scipy.fftpack.ifftn(Vx_r,overwrite_x=True); del Vx_r
Vy_k = scipy.fftpack.ifftn(Vy_r,overwrite_x=True); del Vy_r
Vz_k = scipy.fftpack.ifftn(Vz_r,overwrite_x=True); del Vz_r
theta_k = DFL.theta(Vx_k,Vy_k,Vz_k,dims)*2.0*np.pi/BoxSize
del Vx_k, Vy_k, Vz_k