def compute_Pk(T_maps, BoxSize, fout):
Pk = np.zeros((T_maps.shape[0], 45), dtype=np.float64)
for i in range(T_maps.shape[0]):
Pk_real = PKL.Pk_plane(T_maps[i],
BoxSize,
MAS='None',
threads=1,
verbose=False)
k, Pk[i] = Pk_real.k, Pk_real.Pk
np.savetxt(fout, np.transpose([k,
np.mean(Pk, axis=0),
np.std(Pk, axis=0)]))
return k, np.mean(Pk, axis=0), np.std(Pk, axis=0)
# do a loop over all the maps
for i in numbers:
if i % 10000 == 0: print(i)
# get the value of the Pk
Pk_in = A[i] * k_in**B
# generate density field
maps_partial[i] = DFL.gaussian_field_2D(grid, k_in, Pk_in,
Rayleigh_sampling, seed[i],
BoxSize, threads, verbose)
# compute power spectrum
Pk_partial[i] = PKL.Pk_plane(maps_partial[i], BoxSize, MAS, threads,
verbose).Pk
#np.savetxt('Pk1.txt', np.transpose([Pk.k, Pk.Pk, Pk.Nmodes]))
#print(1.0/np.sqrt(np.sum(Pk.Nmodes)))
# make some statistics
#print(np.mean(maps[i]), np.min(maps[i]), np.max(maps[i]))
# make image
"""
fig=figure()
ax1=fig.add_subplot(111)
cax = ax1.imshow(df,cmap=get_cmap('jet'),origin='lower',interpolation='spline36',
extent=[0, BoxSize, 0, BoxSize])
#vmin=min_density,vmax=max_density)
#norm = LogNorm(vmin=min_density,vmax=max_density))
import numpy as np
import sys, os
import Pk_library as PKL
BoxSize = 75.0 #Mpc/h
MAS = 'CIC'
threads = 1
f1 = 'HI_Predicted_Full2.npy'
f2 = 'HI_Full_True.npy'
HI_pred = np.load(f1)
#HI_pred = HI_pred/np.mean(HI_pred) - 1.0
HI_true = np.load(f2)
#HI_true = HI_true/np.mean(HI_true) - 1.0
print HI_pred.shape
print HI_true.shape
print np.min(HI_true), np.max(HI_true), np.mean(HI_true)
print np.min(HI_pred), np.max(HI_pred), np.mean(HI_pred)
Pk = PKL.Pk_plane(HI_true, BoxSize, MAS, threads)
np.savetxt('Pk_true.txt', np.transpose([Pk.k, Pk.Pk]))
Pk = PKL.Pk_plane(HI_pred, BoxSize, MAS, threads)
np.savetxt('Pk_pred.txt', np.transpose([Pk.k, Pk.Pk]))
# get the name of the output file
fpk = '%s/%s/%d/Pk2D_m_z=%s.txt' % (folder_out, cosmo, i, z)
if os.path.exists(fpk): continue
# find the density field file and read it
f_df = '%s/%s/%d/df_z=%s.npy' % (root, cosmo, i, z)
if not (os.path.exists(f_df)): continue
df = np.load(f_df)
# create output folder if it does not exists
if not (os.path.exists('%s/%s/%d' % (folder_out, cosmo, i))):
os.system('mkdir %s/%s/%d' % (folder_out, cosmo, i))
# compute matter Pk
Pk = PKL.Pk_plane(df, BoxSize, MAS, threads)
np.savetxt(fpk, np.transpose([Pk.k, Pk.Pk]))
###### paired fixed realizations ######
for i in numbers:
for pair in [0, 1]:
# get the name of the output file
fpk = '%s/%s/NCV_%d_%d/Pk2D_m_z=%s.txt' % (folder_out, cosmo, pair, i,
z)
if os.path.exists(fpk): continue
# find the density field file and read it
f_df = '%s/%s/NCV_%d_%d/df_z=%s.npy' % (root, cosmo, pair, i, z)
if not (os.path.exists(f_df)): continue
