def main():
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
t_0 = MPI.Wtime()
N_dset = (nrbin + 1) * nrbin // 2 # numbe of C^ij(l) data sets
#data_type_size = 8 # number of bytes for double precison data
zbin = np.zeros(nrbin +
1) # for both zbin and chibin, the first element is 0.
chibin = np.zeros(nrbin + 1)
shape_noise = np.zeros(nrbin)
num_kin = 506 # the number of boundary points of k bins from the input matter power spectrum file
# consider num_kbin as the input number of k bins
num_kbin = num_kin - 1 # k_max should be larger than lmax/xmax, -1 means disregarding the last term
k_par = np.zeros(num_kbin) # Input k and Pk for the calculation of C^ij(l)
Pk_par = np.zeros(num_kbin)
# l (the parameter of C^ij(l)) value equals to l_min, l_min+delta_l, ..., l_max-delta_l
# We choose the case below:
l_max = 2002 # l_max < X_max*k_max
#l_max = 22
l_min = 1
delta_l = 3
num_l = (l_max - l_min) // delta_l + 1
c = 2.99792458e5 # speed of light unit in km/s
H_0 = 100.0 # unit: h * km/s/Mpc
sigmae = 0.021 # Tully-Fisher case \sigma_e from Eric's paper
scale_n = 1.10 # Tully-Fisher total surface number density (unit: arcmin^-2), from Eric et al.(2013), Table 2 (TF-Stage)
cross_const = (1.5 * cosmic_params.omega_m)**2.0 * (
H_0 / c
)**4.0 # It's the coefficent constant of convergence power spectrum, see Eq.(21)
#print 'cross_const', cross_const
sr_const = np.pi**2.0 / 1.1664e8 # 1 square acrminute = sr_const steradian
constx = sr_const / cross_const # The constx connects shot noise with C^ij(l)
idir0 = '/Users/ding/Documents/playground/shear_ps/SVD_ps/'
inputf = idir0 + 'Input_files/nz_stage_IV.txt' # Input file of n(z) which is the galaxy number density distribution in terms of z
# Here center_z denotes z axis of n(z). It may not be appropriate since we don't have redshift bin setting
center_z, n_z = np.loadtxt(inputf, dtype='f8', comments='#', unpack=True)
spl_nz = InterpolatedUnivariateSpline(center_z, n_z)
n_sum = spl_nz.integral(center_z[0],
center_z[-1]) # Calculate the total number density
#print(n_sum)
scale_dndz = scale_n / n_sum
n_z = n_z * scale_dndz # rescale n(z) to match the total number density from the data file equal to scale_n
spl_nz = InterpolatedUnivariateSpline(
center_z, n_z) # Interpolate n(z) in terms of z using spline
#nz_test = interpolate.splev(center_z, tck_nz, der=0)
#print(abs(n_z- nz_test)<1.e-7)
# calculate total number density n^i (integrate dn/dz) in the ith tomographic bin
def n_i_bin(zbin, i):
zi = zbin[i]
zf = zbin[i + 1]
# rescale n(z) to match the total number density from the data file equal to scale_n
##n_i = scale_dndz * integrate.quad(n_z, zi, zf, epsabs=1.e-7, epsrel=1.e-7)[0]
n_i = spl_nz.integral(zi, zf)
return n_i
G_0 = growth_factor(
0.0, cosmic_params.omega_m) # G_0 at z=0, normalization factor
num_z = np.size(
center_z) # the number of z bins of n(z), obtained from the data file
chi_z = np.zeros(num_z)
for i in range(num_z):
chi_z[i] = comove_d(
center_z[i]
) * c / H_0 # with unit Mpc/h, matched with that of ell/k
# we want interpolate z as a function of chi
spl_zchi = InterpolatedUnivariateSpline(chi_z,
center_z) # z as a function of chi
# here interpolate \chi as a function of z
spl_chiz = InterpolatedUnivariateSpline(center_z, chi_z)
# bin interval
z_min = center_z[0]
z_max = 2.0 # based on the data file, at z=2.0, n(z) is very small
zbin_avg = (z_max - z_min) / float(nrbin)
for i in range(nrbin):
zbin[i] = i * zbin_avg + z_min
zbin[-1] = z_max
# print('Xmax', c/H_0*comove_d(zbin[-1]))
# print('nbar first element: ', n_i_bin(zbin, 0))
# Note that here chibin[0] is not equal to 0, since there is redshift cut at low z. Unit is Mpc/h
for i in range(0, nrbin + 1):
chibin[i] = comove_d(zbin[i]) * c / H_0
# 3D power spectrum is obtained from CAMB using the above cosmological parameters.
##inputf = fpath+'test_matterpower.dat'# if it's used, the cosmological parameters should also be changed correspondingly.
inputf = idir0 + 'Input_files/CAMB_Planck2015_matterpower.dat'
k_camb, Pk_camb = np.loadtxt(inputf, dtype='f8', comments='#', unpack=True)
Pk_camb_spl = InterpolatedUnivariateSpline(k_camb, Pk_camb)
ifile = idir0 + 'Input_files/transfer_fun_Planck2015.dat'
kk, Tf = np.loadtxt(ifile,
dtype='f8',
comments='#',
usecols=(0, 1),
unpack=True)
##print(kk)
k_0 = 0.001 # unit h*Mpc^-1
Pk_0 = Pk_camb_spl(k_0)
Tf_spl = InterpolatedUnivariateSpline(kk, Tf)
Tf_0 = Tf_spl(k_0)
P0_a = Pk_0 / (pow(k_0, cosmic_params.ns) * Tf_0**2.0)
Psm_transfer = P0_a * pow(
k_camb, cosmic_params.ns
) * Tf**2.0 # Get primordial (smooth) power spectrum from the transfer function
Pk_now_spl = InterpolatedUnivariateSpline(k_camb, Psm_transfer)
# ------ This part calculates the Sigma^2_{xy} using Pwig from CAMB. -------#
z_mid = z_max / 2.0
q_BAO = 110.0 # unit: Mpc/h, the sound horizon scale
Sigma2_integrand = lambda k: Pk_camb_spl(k) * (1.0 - np.sin(k * q_BAO) /
(k * q_BAO))
pre_factor = 1.0 / (3.0 * np.pi**2.0) * (
growth_factor(z_mid, cosmic_params.omega_m) / G_0)**2.0
Sigma2_xy = pre_factor * integrate.quad(
Sigma2_integrand, k_camb[0], k_camb[-1], epsabs=1.e-03,
epsrel=1.e-03)[0]
print('At z=', z_mid, 'Sigma2_xy=', Sigma2_xy)
#----------------------------------------------------------------------------#
def Pk_par_integrand(k):
if Pk_type == 'Pwig_linear':
Pk_par = Pk_camb_spl(k)
elif Pk_type == 'Pnow':
Pk_par = Pk_now_spl(k)
elif Pk_type == 'Pwig_nonlinear':
Pk_par = Pk_now_spl(k) + (Pk_camb_spl(k) - Pk_now_spl(k)) * np.exp(
-k**2.0 * Sigma2_xy / 2.0)
return Pk_par
odir1 = 'mpi_preliminary_data_{}/'.format(Pk_type)
if Psm_type == 'Pnow':
odir1_Gm = odir1 + 'set_Pnorm_Pnow/'
else:
odir1_Gm = odir1
odir = odir0 + odir1 + 'comm_size{}/'.format(size)
odir_Gm = odir0 + odir1_Gm + 'comm_size{}/'.format(size)
if rank == 0:
if not os.path.exists(odir):
os.makedirs(odir)
if not os.path.exists(odir_Gm):
os.makedirs(odir_Gm)
comm.Barrier()
print('odir_Gm:', odir_Gm, 'from rank:', rank)
prefix = 'Tully-Fisher'
Cijl_outf_prefix = odir + prefix # The prefix of output file name
Gm_outf_prefix = odir_Gm + prefix
iu1 = np.triu_indices(
nrbin) # Return the indices for the upper-triangle of an (n, m) array
#------------------------------------------------
def get_shapenoise(rank):
if rank == 0:
# Calculate covariance matrix of Pk, the unit of number density is per steradians
for i in range(nrbin):
shape_noise[i] = sigmae**2.0 / n_i_bin(zbin, i)
pseudo_sn = shape_noise * constx
# Output the shape noise (includes the scale factor) in a file
outf = odir0 + odir1 + prefix + '_pseudo_shapenoise_{0}rbins.out'.format(
nrbin) # basic variable
np.savetxt(outf, pseudo_sn, fmt='%.15f', newline='\n')
#---------------------------------------------------
# Interpolate g^i in terms of chi (comoving distance)
ifile = './lens_eff_g_i/g_i_{}rbins.npz'.format(nrbin)
npz_data = np.load(ifile)
chi_array = npz_data['chi_array'] * c / H_0
g_i_matrix = npz_data['g_i_matrix']
spl_gi_list = []
for i in range(nrbin):
spl_gi = InterpolatedUnivariateSpline(chi_array, g_i_matrix[i, :])
spl_gi_list.append(spl_gi)
ifile = idir0 + 'Input_files/KW_stage_IV_num_ell_per_rank_comm_size{}.dat'.format(
size)
num_ell_array = np.loadtxt(ifile, dtype='int', comments='#', usecols=(1, ))
num_l_in_rank = num_ell_array[rank]
#------------------------------------------------------------------------------------------------#
#--------------------------- Part 1: calculate C^ij(l) ------------------------------------------#
# Note: Don't generate output G matrix for output P(k) in the process with C^ij(l), because the interval of k bins are
# different from those of the G matrix for 'observered' data C^ij(l)!
#------------------------------------------------------------------------------------------------#
# This is for output G' matrix.
def Gm_integrand_out(k, c_i, spl_gi, spl_gj, ell):
chi_k = ell / k
# Since the diameter of Milky Way is about 0.03 Mpc, we assume that the smallest interval between chi_k and chibin[i+1] larger than 0.1 Mpc/h.
if (chibin[c_i + 1] - 1.e-8) < chi_k:
return 0.0
else:
#z_k = interpolate.splev(chi_k, tck_zchi, der=0)
z_k = spl_zchi(chi_k)
GF = (growth_factor(z_k, cosmic_params.omega_m) / G_0)**2.0
return (1.0 + z_k
)**2.0 * spl_gi(chi_k) * spl_gj(chi_k) * ell / k**2.0 * GF
# This is for output C^{ij}(l).
def Gm_integrand_in(k, c_i, spl_gi, spl_gj, ell):
return Gm_integrand_out(k, c_i, spl_gi, spl_gj,
ell) * Pk_par_integrand(k)
def get_Cijl(comm, rank):
# Output the Cij_l array in which each term is unique.
def cal_cijl(l, rank):
#n_l = default_num_l_in_rank * rank + l
n_l = np.sum(num_ell_array[0:rank]) + l
ell = l_min + n_l * delta_l
ell = ell * alpha
#offset_cijl = n_l * N_dset * data_type_size
c_temp = np.zeros((nrbin, nrbin))
for c_i in range(nrbin):
for c_j in range(c_i, nrbin):
# we could use smaller epsrel, but it would require more integration points to achieve that precision.
res = integrate.quad(Gm_integrand_in,
k_camb[0],
k_camb[-1],
args=(c_i, spl_gi_list[c_i],
spl_gi_list[c_j], ell),
limit=200,
epsabs=1.e-6,
epsrel=1.e-12)
c_temp[c_i][c_j] = res[0]
abserr = res[1]
if res[0] != 0.0:
relerr = abserr / res[0]
else:
relerr = 0.0
#c_temp[c_i][c_i : nrbin] = np.dot(gmatrix_jk, Pk_par)
array_cij = np.asarray(
c_temp[iu1],
dtype=np.float64) # extract upper-triangle of c_temp
if rank == 0:
#print('rank:', rank, 'array_cij:', array_cij)
print('ell from rank', rank, 'is', ell,
'abs err of Cijl is %.4e' % abserr,
'and rel err is %.4e' % relerr)
return ell, array_cij, abserr, relerr
Cijl_file = Cijl_outf_prefix + '_Cij_l_{0}rbins_{1}kbins_CAMB_rank{2}.bin'.format(
nrbin, num_kbin, rank) # basic variable
Cijl_fwriter = open(Cijl_file, 'wb')
err_info = np.array([], dtype=np.float64).reshape(0, 3)
for l in range(num_l_in_rank):
ell, cijl, abserr, relerr = cal_cijl(l, rank)
cijl.tofile(Cijl_fwriter, sep="")
err_info = np.vstack((err_info, np.array([ell, abserr, relerr])))
Cijl_fwriter.close()
err_info_ofile = Cijl_outf_prefix + '_integration_error_Cij_l_{0}rbins_{1}kbins_CAMB_rank{2}.out'.format(
nrbin, num_kbin, rank)
np.savetxt(err_info_ofile,
err_info,
fmt='%i %.4e %.4e',
delimiter=' ',
newline='\n',
header='ell abs_err rel_err',
comments='#')
#comm.Barrie()
#-----------------------------------------------------------------------------------------------#
#------------------------- Part 2: get Gm_cross_out for output P(k) ----------------------------#
######------------- set up output k space and G' matrix for output Pk ----------------###########
def get_Gm_out(comm, rank):
# construct Gmatrix: Gout for output Pk with num_kout kbins
# Note: The algorithm is the same as that calculating C^ij(l) in Part 1. Here we use a simplified (looks like) way to get Gmatrix_l.
def cal_G(l, rank):
n_l = np.sum(num_ell_array[0:rank]) + l
ell = l_min + n_l * delta_l
ell = ell * alpha
#offset_Gm = n_l * N_dset * num_kout * data_type_size
Gmatrix_l = np.zeros((N_dset, num_kout))
# j denotes column
for j in range(num_kout):
# i denotes row
for i in range(N_dset):
# redshift bin i: rb_i
rb_i = iu1[0][i]
# in python, eps should be larger than 1.e-15, to be safe. The smallest chi from the corresponding output k bin should be smaller than the
# the upper boundary of chi from the ith tomographic bin
if chibin[rb_i + 1] > ell / kout[j + 1]:
##krb_i = ell/(chibin[rb_i]+1.e-12) # avoid to be divided by 0
rb_j = iu1[1][i]
# more precise calculation of Gmatrix_l
# the j index of Pnorm_out denotes k bin id, different from the index rb_j of g_j
res = integrate.quad(Gm_integrand_out,
kout[j],
kout[j + 1],
args=(rb_i, spl_gi_list[rb_i],
spl_gi_list[rb_j], ell),
epsabs=1.e-6,
epsrel=1.e-6)
Gmatrix_l[i][j] = res[0] * Pnorm_out[j]
abserr = res[1]
if res[0] != 0.0:
relerr = abserr / res[0]
else:
relerr = 0.0
#print Gmatrix_l[:, 0]
if rank == 0:
#print('rank:', rank, 'Gm:', Gmatrix_l)
print('ell from rank', rank, 'is', ell,
'abs err of G is %.4e' % abserr,
'rel err is %.4e' % relerr)
return ell, Gmatrix_l, abserr, relerr
kout, k_mid = np.zeros(num_kout + 1), np.zeros(num_kout)
k_low, k_high = 0.01, 1.0 # This set may need to be checked more!
kout[0], kout[1], kout[-1] = k_camb[0], k_low, k_camb[-1]
lnk_factor = np.log(k_high / k_low) / (num_kout - 2)
for i in range(2, num_kout):
kout[i] = kout[i - 1] * np.exp(lnk_factor)
#print kout
for i in range(num_kout):
k_mid[i] = (kout[i] + kout[i + 1]) / 2.0
if Psm_type == 'Pnorm' or Psm_type == 'default':
Pnorm_out = 1.5e4 / (
1.0 + (k_mid / 0.05)**
2.0)**0.65 # from Eisenstein & Zaldarriaga (2001)
elif Psm_type == 'Pnow':
Pnorm_out = Pk_now_spl(
k_mid
) # Test how the change of Pnow could influence the eigenvalues from SVD routine.
# Gm_cross_out uses selected new k bins
Gm_cross_file = Gm_outf_prefix + '_Gm_cross_out_{0}rbins_{1}kbins_CAMB_rank{2}.bin'.format(
nrbin, num_kout, rank) # basic variable
Gm_cross_fwriter = open(Gm_cross_file, 'wb')
err_info = np.array([], dtype=np.float64).reshape(0, 3)
for l in range(num_l_in_rank):
ell, Gm, abserr, relerr = cal_G(l, rank)
Gm.tofile(Gm_cross_fwriter, sep="")
err_info = np.vstack(
(err_info, np.array([ell, abserr, relerr]))
) # If relerr = xx/0, it doesn't seem to be appendable on array.
Gm_cross_fwriter.close()
err_info_ofile = Gm_outf_prefix + '_integration_error_Gm_cross_out_{0}rbins_{1}kbins_CAMB_rank{2}.out'.format(
nrbin, num_kbin, rank)
np.savetxt(err_info_ofile,
err_info,
fmt='%i %.4e %.4e',
delimiter=' ',
newline='\n',
header='ell abs_err rel_err',
comments='#')
if cal_sn == "True":
get_shapenoise(rank)
if cal_cijl == "True":
get_Cijl(comm, rank)
#comm.Barrier()
t_1 = MPI.Wtime()
if cal_Gm == "True" and Pk_type == 'Pwig_nonlinear':
get_Gm_out(comm, rank)
#comm.Barrier()
t_2 = MPI.Wtime()
if rank == 0:
print('Running time for Cijl:', t_1 - t_0)
print('Running time for G matrix:', t_2 - t_1)
#######################################################
def plot_numd_spectroscopy():
odir_data = "./numd_distribute_spectro/"
if not os.path.exists(odir_data):
os.makedirs(odir_data)
odir_fig = odir_data + 'nz_fig/'
if not os.path.exists(odir_fig):
os.makedirs(odir_fig)
nd_avg = []
for i in range(nrbin):
nd_avg.append(n_i_bin(zbin, i) / (zbin[i + 1] - zbin[i]))
ofile = odir_data + 'gal_numden_spectroz_{}rbins.out'.format(nrbin)
header_line = ' bin_boundary(low) nz_avg'
np.savetxt(ofile,
np.array([zbin[0:-1], nd_avg]).T,
fmt='%.7f',
newline='\n',
comments='#')
print("nd_avg:", nd_avg, "zbin:", zbin)
fig, ax = plt.subplots(figsize=(8, 6))
bars = ax.bar(left=zbin[0:-1],
height=nd_avg,
width=zbin_avg,
align='edge',
color='white',
edgecolor='grey')
bars[11].set_color('r')
print(bars)
# n, bins, pathes = ax.hist(nd_avg, bins=nrbin, range=[zbin[0], zbin[-1]], align='left')
# print(n, bins, pathes)
ax.plot(center_z, n_z, 'k-', lw=2.0)
ax.set_xlim([0.0, z_max])
ax.set_ylim([0.0, 1.0])
ax.set_xlabel(r'$z$', fontsize=20)
#ax.set_ylabel('$n^i(z)$ $[\mathtt{arcmin}]^{-2}$', fontsize=20)
ax.set_ylabel(r'$dn^i/dz \; [\mathtt{arcmin}]^{-2}$', fontsize=20)
ax.minorticks_on()
ax.tick_params('both', length=5, width=2, which='major', labelsize=15)
ax.tick_params('both', length=3, width=1, which='minor')
ax.set_title("KWL-Stage IV", fontsize=20)
plt.tight_layout()
figname = "gal_numden_{}rbins_spectro.pdf".format(nrbin)
plt.savefig(odir_fig + figname)
plt.show()
plt.close()
if show_nz == "True" and rank == 0:
plot_numd_spectroscopy()
def main():
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
num_kin = 506 # number of k point in the input matter power spectrum file
# l value from l_min, l_min+delta_l, ..., l_max-delta_l
l_max = 2002 # lmax<Xmax*k_max
l_min = 1
delta_l = 3
num_l = (l_max - l_min) // delta_l + 1
# index of kmin from CAMB
kpar_min = 0
# k_max should be larger than lmax/xmax. -1 means disregarding the last term. Basically it's the number of k bins from input.
num_par = num_kin - kpar_min - 1
# dimensin of parameter vector
k_par = np.zeros(num_par)
Pk_par = np.zeros(num_par)
Pnorm = np.zeros(num_par)
c = 2.99792458e5 # speed of light unit in km/s
##----- in traditational WL, sigmae is larger, equal to 0.26 from Table 2 in Eric et al. 2013 ----#
sigmae = 0.26
# For PW-Stage III, the constant of sigma_z (systematic error of source redshift from photometry)
sigma_z_const = 0.1
scale_n = 10.0
# In this code, the constant of cross power spectrum is
cross_const = (1.5 * cosmic_params.omega_m)**2.0 * (100 / c)**4.0
#print 'cross_const', cross_const
# 1 square acrminute = sr_const steradian
sr_const = np.pi**2.0 / 1.1664e8
constx = sr_const / cross_const
# input galaxy number density n(z) file
idir0 = '/Users/ding/Documents/playground/shear_ps/SVD_ps/'
inputf = idir0 + 'Input_files/zdistribution_DES_Tully_Fisher.txt'
lower_z, center_z, upper_z, n_z = np.loadtxt(inputf,
dtype='f8',
comments='#',
unpack=True)
n_sum = np.sum(n_z * (upper_z - lower_z))
#print(n_sum)
scale_dndz = 1.0 / n_sum # Normalize n(z) distribution
n_z = n_z * scale_dndz # rescale n(z) to match the total number density from the data file equal to 1.0/arcmin^2
## spline n_z as a function of z
tck_nz = interpolate.splrep(center_z, n_z)
zmax = center_z[-1]
n_p_zmax = interpolate.splev(zmax, tck_nz,
der=1) # the first derivative at z_max~1.3
# fitting function nc1 * exp(nc2*z)
#nc2 = n_p_zmax/n_z[-1]
#nc1 = n_z[-1]/math.exp(nc2 * center_z[-1])
# fitting function using nc1*z^2*exp(-z^2/nc2), Ma, Z. et al. 2006
nc2 = 2.0 * zmax**2.0 * n_z[-1] / (2.0 * n_z[-1] - zmax * n_p_zmax)
nc1 = n_z[-1] / (zmax**2.0 * math.exp(-zmax**2.0 / nc2))
#print("z_0^2: ", nc2, "Const: ", nc1)
# set zmax as the maximum z after extension
zmax_ext = zmax + math.sqrt(2.0) * 0.05 * (
1 + zmax) * 2.2 # 2.2 is from erf(2.2) which is close to 1.0
print('zmax_ext: ', zmax_ext) # It's about 1.655.
# add 0.001 which is just a small number, so zext[0] doesn't coincide with center_z[-1]
zext = np.linspace(
center_z[-1] + 0.001, zmax_ext,
80) # extended z region for the external tomographic bins
nzext = nc1 * zext**2.0 * np.exp(-zext**2.0 / nc2)
#print('n(zmax):', nzext[-1]) # it's around 1.64
center_z = np.append(center_z, zext)
n_z = np.append(n_z, nzext)
num_z = len(center_z) # set the new num_z
chi_z = np.zeros(num_z)
nz_y2 = np.zeros(num_z)
tck_nz = interpolate.splrep(center_z, n_z) # do a new spline interpolation
##--for traditational WL, the total distribution of galaxies n(z) in tomographic bins is unchanged regardless how complicate the photo-z probability distribution is --#
# calculate the number density nbar^i (integrate dn/dz) in the ith tomographic bin
# the unit of number density is per steradian
def n_i_bin(zbin, i):
zi = zbin[i]
zf = zbin[i + 1]
n_i = interpolate.splint(zi, zf, tck_nz)
return n_i
##-----set tomographic bins-------------##
chi_z = np.zeros(num_z)
for i in range(num_z):
chi_z[i] = comove_d(center_z[i]) * c / 100.0
# we want interpolate z as a function of chi
tck_zchi = interpolate.splrep(chi_z, center_z)
tck_chiz = interpolate.splrep(center_z, chi_z)
#**** Different from the previous case that zmin is 0, zmin is not 0 in the new n(z) file. ****#
zmin = center_z[0]
zbin_avg = (zmax - zmin) / float(num_rbin) # bin interval
nbin_ext = int(zmax_ext / zbin_avg)
#print('# of redshift bins (extended): ', nbin_ext)
# for zbin and chibin, the first element is 0.
zbin = np.zeros(nbin_ext + 1)
chibin = np.zeros(nbin_ext + 1)
for i in range(nbin_ext + 1):
zbin[i] = i * zbin_avg + zmin
# Just note that chibin[0] and chibin[1] store the first bin's up boundaries
chibin[i] = interpolate.splev(zbin[i], tck_chiz, der=0)
G_0 = growth_factor(
0.0, cosmic_params.omega_m) # G_0 at z=0, normalization factor
# 3D power spectrum is from CAMB
##inputf = '../test_matterpower.dat'
inputf = idir0 + 'Input_files/CAMB_Planck2015_matterpower.dat'
k_camb, Pk_camb = np.loadtxt(inputf, dtype='f8', comments='#', unpack=True)
Pk_camb_spl = InterpolatedUnivariateSpline(k_camb, Pk_camb)
ifile = idir0 + 'Input_files/transfer_fun_Planck2015.dat'
kk, Tf = np.loadtxt(ifile,
dtype='f8',
comments='#',
usecols=(0, 1),
unpack=True)
k_0 = 0.001 # unit h*Mpc^-1
Pk_0 = Pk_camb_spl(k_0)
Tf_spl = InterpolatedUnivariateSpline(kk, Tf)
Tf_0 = Tf_spl(k_0)
P0_a = Pk_0 / (pow(k_0, cosmic_params.ns) * Tf_0**2.0)
Psm_transfer = P0_a * pow(
k_camb, cosmic_params.ns
) * Tf**2.0 # Get primordial (smooth) power spectrum from the transfer function
Pk_now_spl = InterpolatedUnivariateSpline(k_camb, Psm_transfer)
# ------ This part calculates the Sigma^2_{xy} using Pwig from CAMB. -------#
z_mid = zmax / 2.0
q_BAO = 110.0 # unit: Mpc/h, the sound horizon scale
Sigma2_integrand = lambda k: Pk_camb_spl(k) * (1.0 - np.sin(k * q_BAO) /
(k * q_BAO))
pre_factor = 1.0 / (3.0 * np.pi**2.0) * (
growth_factor(z_mid, cosmic_params.omega_m) / G_0)**2.0
Sigma2_xy = pre_factor * integrate.quad(
Sigma2_integrand, k_camb[0], k_camb[-1], epsabs=1.e-06,
epsrel=1.e-06)[0]
print('At z=', z_mid, 'Sigma2_xy=', Sigma2_xy)
for i in range(num_par):
k_par[i] = (k_camb[i] + k_camb[i + 1]) / 2.0
# We didn't include 'now' type here as was did in Tully-Fisher case.
if Pk_type == 'Pwig_linear':
Pk_par[i] = Pk_camb_spl(k_par[i])
elif Pk_type == 'Pnow':
Pk_par[i] = Pk_now_spl(k_par[i])
elif Pk_type == 'Pwig_nonlinear':
Pk_par[i] = Pk_now_spl(
k_par[i]) + (Pk_camb_spl(k_par[i]) - Pk_now_spl(
k_par[i])) * np.exp(-k_par[i]**2.0 * Sigma2_xy / 2.0)
odir0 = args.odir0
if alpha != None:
odir0 = odir0 + 'BAO_alpha_{}/'.format(alpha)
odir = odir0 + 'mpi_preliminary_data_{}/comm_size{}/'.format(Pk_type, size)
prefix = 'TW_zext_'
outf_prefix = odir + prefix
if rank == 0:
if not os.path.exists(odir):
os.makedirs(odir)
comm.Barrier()
def get_shapenoise():
shape_noise = np.zeros(nbin_ext)
# Calculate covariance matrix of Pk, the unit of number density is per steradians
for i in range(nbin_ext):
shape_noise[i] = sigmae**2.0 / (scale_n * n_i_bin(zbin, i))
#shape_noise[i] = sigmae**2.0/ s_nz[i] # It's the serious bug that I made and couldn't find it for half a year!
pseudo_sn = shape_noise * constx
# put the shape noise (includes the scale factor) in a file
outf = odir0 + 'mpi_preliminary_data_{}/'.format(
Pk_type) + prefix + 'pseudo_shapenoise_{0}rbins_ext.out'.format(
nbin_ext) # basic variable
np.savetxt(outf, pseudo_sn, fmt='%.15f', newline='\n')
ifile = idir0 + 'Input_files/PW_stage_num_ell_per_rank_comm_size{}.dat'.format(
size) # num of ell is roughly estimated
num_ell_array = np.loadtxt(ifile, dtype='int', comments='#', usecols=(1, ))
num_l_in_rank = num_ell_array[rank]
data_type_size = 8
N_dset = (nbin_ext + 1) * nbin_ext // 2
iu1 = np.triu_indices(nbin_ext)
#
##################################################################################################################
#------------------------------------ get cross power spectrum C^ij(l) ------------------------------------------#
##################################################################################################################
# If we use an up-triangle matrix to store C^ij(l) with nbin_ext redshift bins, it's easier to extract C^ij(l) within numb_bin spectroscopic bins.
# But to save space, I used array form to store C^ij(l) for each l.
def get_Cijl(comm, rank):
def cal_cijl(l, rank):
#n_l = default_num_l_in_rank * rank + l
n_l = np.sum(num_ell_array[0:rank]) + l
ell = l_min + n_l * delta_l
ell = alpha * ell
##offset_cijl = n_l * N_dset * data_type_size
c_temp = np.zeros((nbin_ext, nbin_ext))
for c_i in range(nbin_ext):
g_nr = nbin_ext - c_i
#print('g_nr:', g_nr)
# gmatrix_jk is used to store integration elements to calculate array C^ij(l) from Pk for a certain l
# j=i, i+1,...nbin_ext; j in the name gmatrix_jk also denotes row index
gmatrix_jk = np.zeros((g_nr, num_par))
#print(gmatrix_jk)
# g_col is the column index of gmatrix_jk
for g_col in range(num_par):
chi_k = ell / k_par[g_col]
z_k = interpolate.splev(
chi_k, tck_zchi, der=0
) # Here z_k is understood as the spectroscopic redshift z
g_i = lens_eff(zbin, center_z, n_z, nz_y2, c_i, z_k,
sigma_z_const)
if z_k < zmax: # zmax corresponding to \chi_h in the expression of C^ij(l)
GF = (growth_factor(z_k, cosmic_params.omega_m) /
G_0)**2.0
#print('zmax, z_k, GF:', zmax, z_k, GF)
c_j = c_i
# here g_row is the row index of gmatrix_jk
for g_row in range(g_nr):
g_j = lens_eff(zbin, center_z, n_z, nz_y2, c_j,
z_k, sigma_z_const)
gmatrix_jk[g_row][g_col] = pow(
(1.0 + z_k), 2.0) * g_i * g_j * ell * (
1.0 / k_camb[g_col] -
1.0 / k_camb[g_col + 1]) * GF
###gmatrix_jk[g_row][g_col] = pow((1.0+z_k), 2.0)*lens_eff(c_i, chi_k)*lens_eff(c_j, chi_k)*ell*(1.0/k_par[g_col]-1.0/k_par[g_col+1])*GF
c_j += 1
c_temp[c_i][c_i:nbin_ext] = np.dot(gmatrix_jk, Pk_par)
# print(c_temp)
cijl = np.asarray(
c_temp[iu1],
dtype=np.float64) # extract upper-triangle of c_temp
if rank == 0:
print('ell from rank', rank, 'is', ell)
return cijl
#eps = zbin_avg/10.0 # set minimum interval for g^i integration, z_max-z>eps !maybe I need to consider this, 04/25/2016
# Output the Cij_l array in which each term is unique.
Cijl_file = outf_prefix + 'Cij_l_{}rbins_ext_{}kbins_CAMB_rank{}.bin'.format(
nbin_ext, num_par, rank) # basic variable
# open file, write and append data in binary format (save storing volume)
Cijl_fwriter = open(Cijl_file, 'wb')
for l in range(num_l_in_rank):
cijl = cal_cijl(l, rank)
cijl.tofile(Cijl_fwriter, sep="")
Cijl_fwriter.close()
##################################################################################################################
#####################-------------------get output k space and G' matrix for output Pk-----------#################
##################################################################################################################
def get_Gm_out(comm, rank):
# odir_list = ['./Gm_cross_out_linear_k_data/', './Gm_cross_out_exp_k_data/']
case = 1 # output k exponentially distributed
kout, k_mid, Pnorm_out = np.zeros(num_kout + 1), np.zeros(
num_kout), np.zeros(num_kout)
# Try k bins linearly distributed
if case == 0:
delta_k = (k_camb[-1] - k_camb[0]) / num_kout
kout[0] = k_camb[0]
for i in range(num_kout):
kout[i + 1] = kout[i] + delta_k
k_mid[i] = (kout[i + 1] + kout[i]) / 2.0
Pnorm_out[i] = 1.5e4 / (1.0 + (k_mid[i] / 0.05)**2.0)**0.65
# Try the simplest case, using the trapezoidal rule. Try to use exponentially distributed k bins
elif case == 1:
k_low, k_high = 0.01, 1.0
kout[0], kout[1], kout[-1] = k_camb[0], k_low, k_camb[-1]
lnk_factor = np.log(k_high / k_low) / (num_kout - 2)
for i in range(2, num_kout):
kout[i] = kout[i - 1] * np.exp(lnk_factor)
#print(kout)
for i in range(num_kout):
k_mid[i] = (kout[i] + kout[i + 1]) / 2.0
Pnorm_out[i] = 1.5e4 / (
1.0 + (k_mid[i] / 0.05)**2.0
)**0.65 # This Pnorm is from Eisenstein & Zaldarriaga 1999.
# construct Gmatrix: Gout for output Pk with num_kout kbins
def cal_Gm(l, rank):
#n_l = default_num_l_in_rank * rank + l
n_l = np.sum(num_ell_array[0:rank]) + l
ell = l_min + n_l * delta_l
ell = alpha * ell
##offset_Gm = n_l * N_dset * num_kout * data_type_size
Gmatrix_l = np.zeros((N_dset, num_kout))
# j denotes column
for j in range(num_kout):
#chi_k: comoving distance from k
chi_k = ell / k_mid[
j] # I would have to say I could only do approximation here, e.g., using k_mid
z_k = interpolate.splev(chi_k, tck_zchi, der=0)
# i denotes row
if z_k < zmax:
GF = (growth_factor(z_k, cosmic_params.omega_m) / G_0)**2.0
for i in range(N_dset):
# redshift bin i: rb_i
rb_i = iu1[0][i]
gi = lens_eff(zbin, center_z, n_z, nz_y2, rb_i, z_k,
sigma_z_const)
# redshift bin j: rb_j
rb_j = iu1[1][i]
gj = lens_eff(zbin, center_z, n_z, nz_y2, rb_j, z_k,
sigma_z_const)
# here too, I did approximation for the integration, e.g., the term (1/k1 - 1/k2)
Gmatrix_l[i][j] = pow(
(1.0 + z_k), 2.0) * gi * gj * ell * (
1.0 / kout[j] -
1.0 / kout[j + 1]) * GF * Pnorm_out[j]
return Gmatrix_l
# Gm_cross_out uses selected new k bins
Gm_cross_file = outf_prefix + 'Gm_cross_out_{}rbins_{}kbins_CAMB_rank{}.bin'.format(
nbin_ext, num_kout, rank) # basic variable
Gm_cross_fwriter = open(Gm_cross_file, 'wb')
for l in range(num_l_in_rank):
Gm = cal_Gm(l, rank)
Gm.tofile(Gm_cross_fwriter, sep="")
Gm_cross_fwriter.close()
def main():
scale_n = 1.10 # Tully-Fisher total surface number density (unit: arcmin^-2), from Eric et al.(2013), Table 2 (TF-Stage)
c = 2.99792458e5 # speed of light unit in km/s
N_dset = (nrbin + 1) * nrbin // 2 # numbe of C^ij(l) data sets
zbin = np.zeros(nrbin +
1) # for both zbin and chibin, the first element is 0.
chibin = np.zeros(nrbin + 1)
eps = 0.1
inputf = '../Input_files/nz_stage_IV.txt' # Input file of n(z) which is the galaxy number density distribution in terms of z
# Here center_z denotes z axis of n(z). It may not be appropriate since we don't have redshift bin setting
center_z, n_z = np.loadtxt(inputf, dtype='f8', comments='#', unpack=True)
spl_nz = InterpolatedUnivariateSpline(center_z, n_z)
n_sum = spl_nz.integral(center_z[0],
center_z[-1]) # Calculate the total number density
#print(n_sum)
scale_dndz = 1.0 / n_sum
n_z = n_z * scale_dndz # Normalize n(z)
spl_nz = InterpolatedUnivariateSpline(
center_z, n_z) # Interpolate n(z) in terms of z using spline
# bin interval
z_min = center_z[0]
z_max = 2.0 # based on the data file, at z=2.0, n(z) is very small
zbin_avg = (z_max - z_min) / float(nrbin)
for i in range(nrbin):
zbin[i] = i * zbin_avg + z_min
if zbin[i] <= z_target:
target_i = i
zbin[-1] = z_max
# Note that here chibin[0] is not equal to 0, since there is redshift cut at low z.
for i in range(0, nrbin + 1):
chibin[i] = comove_d(zbin[i]) * c / 100.0
print('Xmax', c / 100.0 * comove_d(zbin[-1]))
#Xmax = comove_d(zbin[-1])
print('Xmax')
print('target_i:', target_i)
#z_array = np.linspace(0.0, zbin[target_i+1], 1000, endpoint=False)
z_array = np.linspace(0.0, z_max, 1000, endpoint=True)
chi_array = np.array([comove_d(z_i) for z_i in z_array]) * c / 100.0
print("chi_array:", chi_array)
g_i_array = np.array([], dtype=np.float64)
for chi_k in chi_array:
g_i = lens_eff(target_i, chi_k, chibin, eps)
g_i_array = np.append(g_i_array, g_i)
print('min_gi:', np.min(g_i_array), 'at chi=',
chi_array[np.argmin(g_i_array)])
odir0 = '/Users/ding/Documents/playground/shear_ps/project_final/fig_lens_eff/gi_data/'
odir = odir0 + '{}/'.format(survey_stage)
if not os.path.exists(odir):
os.mkdir(odir)
ofile = odir + 'gi_nrbin{}_zk_{}_rbinid_{}.npz'.format(
nrbin, z_target, target_i)
np.savez(ofile, z=z_array, gi=g_i_array)
odir = './figs/lens_eff_gi/'
if not os.path.exists(odir):
os.makedirs(odir)
ofile = odir + 'gi_{}_nrbin{}_zi_{}.pdf'.format(survey_stage, nrbin,
z_target)
plot_lens_eff(z_array, g_i_array, nrbin, z_target, ofile)
ofile = odir + 'gi_{}_nrbin{}_zi_{}_version2.pdf'.format(
survey_stage, nrbin, z_target)
plot_lens_eff_version2(z_array, chi_array, g_i_array, nrbin, z_target,
ofile)
def main():
c = 2.99792458e5 # speed of light unit in km/s
sigma_z_const = 0.05 # for PW-Stage IV, from Table 3 in Eric et al. 2015
#scale_n = 31.0
# input galaxy number density n(z) file
inputf = '../Input_files/nz_stage_IV.txt' # Input file of n(z) which is the galaxy number density distribution in terms of z
# Here center_z denotes z axis of n(z). It may not be appropriate since we don't have redshift bin setting
center_z, n_z = np.loadtxt(inputf, dtype='f8', comments='#', unpack=True)
num_z = len(center_z)
spl_nz = InterpolatedUnivariateSpline(center_z, n_z)
n_sum = spl_nz.integral(center_z[0],
center_z[-1]) # Calculate the total number density
#print(n_sum)
scale_dndz = 1.0 / n_sum
n_z = n_z * scale_dndz # normalize n(z) to match the total number density from the data file equal to 1.0/arcmin^2
tck_nz = interpolate.splrep(center_z, n_z)
zmax = 2.0 # Based on Eric's n(z) data file for Stage IV weak lensing survey, we cut number density at z=2.0, after which n(z) is very small.
#----------------------------------------------------------------------------------------------
#-- Removed this extrapolation part for n(z) since the given data file covers large z range.
# ...
#----------------------------------------------------------------------------------------------
# Set zmax_ext as the maximum z after extension, it's about 2.467 in this case.
zmax_ext = zmax + math.sqrt(2.0) * 0.05 * (
1 + zmax) * 2.2 # 2.2 is from erf(2.2) which is close to 1.0
print('zmax_ext: ', zmax_ext) # It's about 2.467.
nz_y2 = np.zeros(num_z)
#**** Different from the previous case that zmin is 0, zmin is not 0 in the new n(z) file. ****#
zmin = center_z[0]
zbin_avg = (zmax - zmin) / float(nrbin) # bin interval
nbin_ext = int(zmax_ext / zbin_avg)
#print('# of redshift bins (extended): ', nbin_ext)
zbin = np.zeros(nbin_ext + 1)
for i in range(nbin_ext + 1):
zbin[i] = i * zbin_avg + zmin
if zbin[i] <= z_target:
target_i = i
#z_array = np.linspace(0.0, zbin[target_i+1], 1000, endpoint=False)
z_array = np.linspace(0.0, zmax, 1000, endpoint=False)
chi_array = np.array([comove_d(z_i) for z_i in z_array
]) # not including the unit of distance
g_i_array = np.array([], dtype=np.float64)
for z_k in z_array:
g_i = lens_eff(zbin, center_z, n_z, nz_y2, target_i, z_k,
sigma_z_const)
g_i_array = np.append(g_i_array, g_i)
odir0 = '/Users/ding/Documents/playground/shear_ps/project_final/fig_lens_eff/gi_data/'
odir = odir0 + '{}/'.format(survey_stage)
if not os.path.exists(odir):
os.makedirs(odir)
ofile = odir + 'gi_nrbin{}_zk_{}_rbinid_{}.npz'.format(
nrbin, z_target, target_i)
#ofile = './gi_nrbin{}_zk_{}_rbinid_{}_30abscissas.npz'.format(nrbin, z_target, target_i)
#ofile = './gi_nrbin{}_zk_{}_rbinid_{}_50abscissas.npz'.format(nrbin, z_target, target_i)
#ofile = './gi_nrbin{}_zk_{}_rbinid_{}_50abscissas_5sigma.npz'.format(nrbin, z_target, target_i)
#ofile = './gi_nrbin{}_zk_{}_rbinid_{}_50abscissas_largerintlim_in_denominator.npz'.format(nrbin, z_target, target_i)
np.savez(ofile, z=z_array, gi=g_i_array)
odir = './figs/lens_eff_gi/'
if not os.path.exists(odir):
os.makedirs(odir)
ofile = odir + 'gi_nrbin{}_zk_{}.pdf'.format(nrbin, z_target)
plot_lens_eff(z_array, g_i_array, nrbin, z_target, ofile)
ofile = odir + 'gi_nrbin{}_zk_{}_version2.pdf'.format(nrbin, z_target)
plot_lens_eff_version2(z_array, chi_array, g_i_array, nrbin, z_target,
ofile)