K_S_min = 0.
K_S_max = 13.9
zbins = [(0.,0.24)]#, (0.,0.08), (0.08,0.4)]
bias = 1.24
nsims = 500
ncosmo = 100
fsky = 0
# Planck chains
planck_chains_file = '/Users/fbianchini/Downloads/base_planck_lowl_lowLike_highL/base/planck_lowl_lowLike_highL/base_planck_lowl_lowLike_highL_4.txt'
planck_chains = np.loadtxt(planck_chains_file)
# Load Cat
print("...loading 2MPZ catalogue...")
twoMPZ_cat = 'fits/results16_23_12_14_73.fits'
twompz = Load2MPZ(twoMPZ_cat, K_S_min=K_S_min, K_S_max=K_S_max)
print("...done...")
# Data spectra
clkg = {}
clgg = {}
err_clkg = {}
err_clgg = {}
for zbin in zbins:
if fsky:
file_clkg = 'spectra/2MPZ_Planck2015_clkg_dl'+str(delta_ell)+'_lmin_'+str(lmin)+'_lmax'+str(lmax)+'_KS_min_'+str(K_S_min)+'_KS_max_'+str(K_S_max)+'_nside256_zmin_'+str(zbin[0])+'_zmax'+str(zbin[1])+'_split_fsky.dat'
file_clgg = 'spectra/2MPZ_clgg_dl'+str(delta_ell)+'_lmin_'+str(lmin)+'_lmax'+str(lmax)+'_KS_min_'+str(K_S_min)+'_KS_max_'+str(K_S_max)+'_nside256_zmin_'+str(zbin[0])+'_zmax'+str(zbin[1])+'_split_fsky.dat'
else:
file_clkg = 'spectra/2MPZ_Planck2015_clkg_dl'+str(delta_ell)+'_lmin_'+str(lmin)+'_lmax'+str(lmax)+'_KS_min_'+str(K_S_min)+'_KS_max_'+str(K_S_max)+'_nside256_zmin_'+str(zbin[0])+'_zmax'+str(zbin[1])+'_split.dat'
file_clgg = 'spectra/2MPZ_clgg_dl'+str(delta_ell)+'_lmin_'+str(lmin)+'_lmax'+str(lmax)+'_KS_min_'+str(K_S_min)+'_KS_max_'+str(K_S_max)+'_nside256_zmin_'+str(zbin[0])+'_zmax'+str(zbin[1])+'_split.dat'
def main(args):
SetPlotStyle()
# Params 'n' stuff
fits_planck_mask = '../Data/mask_convergence_planck_2015_512.fits'
fits_planck_map = '../Data/convergence_planck_2015_512.fits'
twoMPZ_mask = 'fits/2MPZ_mask_tot_256.fits' # N_side = 256
twoMPZ_cat = 'fits/results16_23_12_14_73.fits'
twoMPZ_cat2 = 'fits/results2_14_20_37_1053.fits.gz'
fits_ebv = 'fits/lambda_sfd_ebv.fits'
nsidelow = 64
nside = args.nside
do_plots = False
delta_ell = args.delta_ell
lmin = args.lmin
lmax = args.lmax
K_S_min = args.K_S_min
K_S_max = args.K_S_max
# lbmin = 2
# lbmax = 10
# Load Cat and Mask
print("...loading 2MPZ catalogue...")
twompz = Load2MPZ(twoMPZ_cat, K_S_min=K_S_min, K_S_max=K_S_max)
twompz2 = Load2MPZ(twoMPZ_cat2, K_S_min=K_S_min, K_S_max=K_S_max)
print("...done...")
# E(B-V) reddening map by Schlegel+ in gal coord at nside=512
ebv = hp.read_map(fits_ebv, verbose=False)
ebv = hp.ud_grade(ebv, nsidelow, pess=True)
A_K = ebv * 0.367 # K-band correction for Galactic extinction
print("...Reddening map loaded...")
# Get nstar map
nstar = GetNstarMap(twompz['RA'],
twompz['DEC'],
twompz['STARDENSITY'],
nsidelow,
rad=True)
# Get 2MPZ mask (A_K + nstar + counts)
# !!! "torque rod gashes" still need to be removed !!!
mask2mpz = Get2MPZMask(A_K,
nstar,
nside=nside,
maskcnt=hpy.GuessMask(twompz['RA'],
twompz['DEC'],
nsidelow,
rad=True))
print("Mask f_sky is %3.2f" % np.mean(mask2mpz))
# Load Planck CMB lensing map 'n' mask
print("...loading Planck map/mask...")
planck_map, planck_mask = LoadPlanck(fits_planck_map,
fits_planck_mask,
nside,
do_plots=0,
filt_plank_lmin=0)
print("...done...")
print("...reading 2MPZ mask...")
mask = hp.read_map(twoMPZ_mask, verbose=False)
nside_mask = hp.npix2nside(mask.size)
if nside_mask != nside:
mask = hp.ud_grade(mask, nside)
print("...done...")
# Common Planck & WISExSCOS mask
mask *= planck_mask
# embed()
# Counts n overdesnity map
cat_tmp = twompz[(twompz.ZPHOTO >= args.zmin)
& (twompz.ZPHOTO < args.zmax)]
a, b = train_test_split(cat_tmp, test_size=0.5)
counts_a = hpy.GetCountsMap(a.RA, a.DEC, nside, coord='G', rad=True)
counts_b = hpy.GetCountsMap(b.RA, b.DEC, nside, coord='G', rad=True)
# hp.write_map('fits/counts_'+zbin+'_sum.fits', counts_sum[zbin])
# hp.write_map('fits/counts_'+zbin+'_diff.fits', counts_diff[zbin])
print("\tNumber of sources in bin [%.2f,%.2f) is %d" %
(args.zmin, args.zmax, np.sum((counts_a + counts_b) * mask)))
print("...converting to overdensity maps...")
delta_a = hpy.Counts2Delta(counts_a, mask=mask)
delta_b = hpy.Counts2Delta(counts_b, mask=mask)
delta_sum = 0.5 * (delta_a + delta_b)
delta_diff = 0.5 * (delta_a - delta_b)
# hp.write_map('fits/delta_'+zbin+'_sum.fits', delta_sum[zbin])
# hp.write_map('fits/delta_'+zbin+'_diff.fits', delta_diff[zbin])
cat_tmp2 = twompz2[(twompz2.ZPHOTO >= args.zmin)
& (twompz2.ZPHOTO < args.zmax)]
a, b = train_test_split(cat_tmp2, test_size=0.5)
counts_a2 = hpy.GetCountsMap(a.RA, a.DEC, nside, coord='G', rad=True)
counts_b2 = hpy.GetCountsMap(b.RA, b.DEC, nside, coord='G', rad=True)
delta_a2 = hpy.Counts2Delta(counts_a2, mask=mask)
delta_b2 = hpy.Counts2Delta(counts_b2, mask=mask)
delta_sum2 = 0.5 * (delta_a2 + delta_b2)
delta_diff2 = 0.5 * (delta_a2 - delta_b2)
print("...done...")
# embed()
# exit()
# XC estimator ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
est_mask = cs.Master(mask,
lmin=lmin,
lmax=lmax,
delta_ell=delta_ell,
MASTER=0,
pixwin=True)
# est_mask2mpz = cs.Master(mask2mpz, lmin=lmin, lmax=lmax, delta_ell=delta_ell, MASTER=0, pixwin=True)
print('...extracting gg spectra...')
clGS, err_gg = est_mask.get_spectra(delta_sum, analytic_errors=True)
clGD = est_mask.get_spectra(delta_diff)
gg = clGS - clGD
clGS_2mpz, err_gg_2mpz = est_mask.get_spectra(delta_sum2,
analytic_errors=True)
clGD_2mpz = est_mask.get_spectra(delta_diff2)
gg_2mpz = clGS_2mpz - clGD_2mpz
# clGS_2mpz, err_gg_2mpz = est_mask2mpz.get_spectra(delta_sum, analytic_errors=True)
# clGD_2mpz = est_mask2mpz.get_spectra(delta_diff)
# gg_2mpz = clGS_2mpz - clGD_2mpz
print('...done...')
# Initializing Cosmo class
cosmo = Cosmo()
cosmo_nl = Cosmo(nonlinear=True)
nz, z, _ = hist(twompz.ZPHOTO, 'knuth', normed=1)
z = 0.5 * (z[1:] + z[:-1])
plt.close()
fig = plt.figure() #figsize=(10, 8))
ax = fig.add_subplot(1, 1, 1)
plt.title(r'$%.2f < z < %.2f$' % (args.zmin, args.zmax))
# z, n_z = GetdNdzNorm(nz=1000)
# clgg = GetAutoSpectrumMagLim( cosmo, 2.21, 0.053, 1.43, bias=1.24, alpha=1., lmax=1000)
# clgg_ph = GetAutoSpectrumMagLim( cosmo_nl, 2.21, 0.053, 1.43, bias=1.24, alpha=1., lmax=1000, sigma_zph=0.03)
# clgg_nl = GetAutoSpectrumMagLim( cosmo_nl, 2.21, 0.053, 1.43, bias=1.24, alpha=1., lmax=1000)
clgg = GetAutoSpectrum(cosmo,
z,
nz, [args.zmin, args.zmax],
b=1.,
alpha=1.,
lmax=500,
sigma_zph=0.015,
i=0)
clgg_nl = GetAutoSpectrum(cosmo_nl,
z,
nz, [args.zmin, args.zmax],
b=1.,
alpha=1.,
lmax=500,
sigma_zph=0.015,
i=0)
# clgg = GetAutoSpectrum(cosmo, z, dndz, [0., 0.08, 0.5], b=1.24, alpha=1., lmax=500, sigma_zph=0.015, i=i)
# clgg_nl = GetAutoSpectrum(cosmo_nl, z, dndz, [0., 0.08, 0.5], b=1.24, alpha=1., lmax=500, sigma_zph=0.015, i=i)
# ax = fig.add_subplot(1,1,i+1)
lab = r'2MPZ $%.1f < K_S < %.1f$' % (K_S_min, K_S_max)
ax.plot(clgg, color='grey', label=r'$b=1$ $\sigma_z=0.015$')
ax.plot(clgg_nl,
color='darkgrey',
ls='--',
label=r'NL $b=1$ $\sigma_z=0.015$')
ax.errorbar(est_mask.lb,
gg,
yerr=err_gg,
fmt='o',
capsize=0,
ms=5,
label=lab)
ax.errorbar(est_mask.lb + 1,
gg_2mpz,
yerr=err_gg_2mpz,
fmt='x',
capsize=0,
ms=5) #, label=lab)
ax.set_ylabel(r'$C_{\ell}^{gg}$')
ax.set_xlabel(r'Multipole $\ell$')
ax.legend(loc='best')
ax.axhline(ls='--', color='grey')
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_xlim([2., 200])
ax.set_yscale('log')
ax.set_ylim([1e-5, 1e-3])
plt.tight_layout()
# plt.savefig('plots/2MPZ_clgg_dl'+str(args.delta_ell)+'_lmin_'+str(args.lmin)+'_lmax'+str(args.lmax)+'_KS_min_'+str(args.K_S_min)+'_KS_max_'+str(args.K_S_max)+'_nside'+str(args.nside)+'_zmin_'+str(args.zmin)+'_zmax'+str(args.zmax)+'_split.pdf', bbox_inches='tight')
plt.show()
# plt.close()
embed()
def main(args):
SetPlotStyle()
# Params 'n' stuff
fits_planck_mask = '../Data/mask_convergence_planck_2015_512.fits'
fits_planck_map = '../Data/convergence_planck_2015_512.fits'
twoMPZ_mask = 'fits/2MPZ_mask_tot_256.fits' # N_side = 256
twoMPZ_cat = 'fits/results16_23_12_14_73.fits'
nside = args.nside
do_plots = False
delta_ell = args.delta_ell
lmin = args.lmin
lmax = args.lmax
K_S_min = args.K_S_min
K_S_max = args.K_S_max
# Load Cat and Mask
print("...loading 2MPZ catalogue...")
twompz = Load2MPZ(twoMPZ_cat, K_S_min=K_S_min, K_S_max=K_S_max)
print("...done...")
# Load Planck CMB lensing map 'n' mask
print("...loading Planck map/mask...")
planck_map, planck_mask = LoadPlanck(fits_planck_map,
fits_planck_mask,
nside,
do_plots=0,
filt_plank_lmin=0)
print("...done...")
print("...reading 2MPZ mask...")
mask = hp.read_map(twoMPZ_mask, verbose=False)
nside_mask = hp.npix2nside(mask.size)
if nside_mask != nside:
mask = hp.ud_grade(mask, nside)
print("...done...")
# Common Planck & WISExSCOS mask
mask *= planck_mask
# Counts 'n' delta map
cat_tmp = twompz[(twompz.ZSPEC >= args.zmin) & (twompz.ZSPEC < args.zmax)]
counts = hpy.GetCountsMap(cat_tmp.RA,
cat_tmp.DEC,
nside,
coord='G',
rad=True)
delta = hpy.Counts2Delta(counts, mask=mask)
est = cs.Master(mask,
lmin=lmin,
lmax=lmax,
delta_ell=delta_ell,
MASTER=1,
pixwin=True)
print('...extracting kg spectra...')
kg, err_kg = est.get_spectra(planck_map, map2=delta, analytic_errors=True)
print("\t=> Detection at rougly %3.2f sigma ") % (np.sum(
(kg[0:] / err_kg[0:])**2)**.5)
# Initializing Cosmo class
cosmo = Cosmo()
cosmo_nl = Cosmo(nonlinear=True)
nz, z, _ = hist(twompz.ZSPEC[twompz.ZSPEC > 0.],
'knuth',
normed=1,
histtype='step')
z = 0.5 * (z[1:] + z[:-1])
plt.close()
fig = plt.figure() #figsize=(10, 8))
ax = fig.add_subplot(1, 1, 1)
plt.title(r'$%.2f < z < %.2f$' % (args.zmin, args.zmax))
clkg = GetCrossSpectrum(cosmo,
z,
nz, [args.zmin, args.zmax],
b=1,
alpha=1.,
lmax=500,
sigma_zph=0.)
clkg_nl = GetCrossSpectrum(cosmo_nl,
z,
nz, [args.zmin, args.zmax],
b=1,
alpha=1.,
lmax=500,
sigma_zph=0.)
# z, n_z = GetdNdzNorm(nz=1000)
# clkg = GetCrossSpectrum(cosmo, z, n_z, [bins_edges[zbin][0],0.3], b=1.24, alpha=1., lmax=1000)
# # clkg = GetCrossSpectrum(cosmo, z, n_z, [bins_edges[zbin][0],bins_edges[zbin][1]], b=1, alpha=1.24, lmax=1000)
# clkg_nl = GetCrossSpectrum(cosmo_nl, z, n_z, [bins_edges[zbin][0],0.5], b=1.24, alpha=1., lmax=1000)
# # clkg_nl = GetCrossSpectrum(cosmo_nl, z, n_z, [bins_edges[zbin][0],bins_edges[zbin][1]], b=1.24, alpha=1., lmax=1000)
# # clgg_pherrz = GetAutoSpectrum(cosmo, z, dndz, [bins_edges[zbin][0],bins_edges[zbin][1]], b=1.24, alpha=1., lmax=1000, sigma_zph=0.03)
lab = r'2MPZ $%.1f < K_S < %.1f$' % (K_S_min, K_S_max)
ax.plot(clkg, color='grey', label=r'$b=1$')
ax.plot(clkg_nl, color='darkgrey', ls='--', label=r'NL $b=1$')
ax.errorbar(est.lb, kg, yerr=err_kg, fmt='o', capsize=0, ms=5, label=lab)
ax.set_ylabel(r'$C_{\ell}^{\kappa g}$')
ax.set_xlabel(r'Multipole $\ell$')
ax.legend(loc='best')
ax.axhline(ls='--', color='grey')
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_xlim([2., 200])
ax.set_yscale('log')
ax.set_ylim([1e-8, 1e-5])
plt.tight_layout()
plt.savefig('plots/2MPZ_Planck_clkg_dl' + str(args.delta_ell) + '_lmin_' +
str(args.lmin) + '_lmax' + str(args.lmax) + '_KS_min_' +
str(args.K_S_min) + '_KS_max_' + str(args.K_S_max) + '_nside' +
str(args.nside) + '_zmin_' + str(args.zmin) + '_zmax' +
str(args.zmax) + '_split_spec.pdf',
bbox_inches='tight')
# plt.show()
# plt.close()
# embed()
np.savetxt(
'spectra/2MPZ_Planck2015_clkg_dl' + str(args.delta_ell) + '_lmin_' +
str(args.lmin) + '_lmax' + str(args.lmax) + '_KS_min_' +
str(args.K_S_min) + '_KS_max_' + str(args.K_S_max) + '_nside' +
str(args.nside) + '_zmin_' + str(args.zmin) + '_zmax' +
str(args.zmax) + '_split_spec.dat', np.c_[est.lb, kg, err_kg])
def main(args):
SetPlotStyle()
# Params 'n' stuff
fits_planck_mask = '../Data/mask_convergence_planck_2015_512.fits'
fits_planck_map = '../Data/convergence_planck_2015_512.fits'
twoMPZ_mask = 'fits/2MPZ_mask_tot_256.fits' # N_side = 256
twoMPZ_cat = 'fits/results16_23_12_14_73.fits'
nside = args.nside
do_plots = False
delta_ell = args.delta_ell
lmin = args.lmin
lmax = args.lmax
K_S_min = args.K_S_min
K_S_max = args.K_S_max
# lbmin = 2
# lbmax = 10
# Load Cat and Mask
print("...loading 2MPZ catalogue...")
twompz = Load2MPZ(twoMPZ_cat, K_S_min=K_S_min, K_S_max=K_S_max)
print("...done...")
# Load Planck CMB lensing map 'n' mask
print("...loading Planck map/mask...")
planck_map, planck_mask = LoadPlanck(fits_planck_map,
fits_planck_mask,
nside,
do_plots=0,
filt_plank_lmin=0)
print("...done...")
print("...reading 2MPZ mask...")
mask = hp.read_map(twoMPZ_mask, verbose=False)
nside_mask = hp.npix2nside(mask.size)
if nside_mask != nside:
mask = hp.ud_grade(mask, nside)
print("...done...")
# Common Planck & WISExSCOS mask
mask *= planck_mask
# embed()
# Counts n overdesnity map
cat_tmp = twompz[(twompz.ZPHOTO >= args.zmin)
& (twompz.ZPHOTO < args.zmax)]
a, b = train_test_split(cat_tmp, test_size=0.5)
counts_a = hpy.GetCountsMap(a.RA, a.DEC, nside, coord='G', rad=True)
counts_b = hpy.GetCountsMap(b.RA, b.DEC, nside, coord='G', rad=True)
# hp.write_map('fits/counts_'+zbin+'_sum.fits', counts_sum[zbin])
# hp.write_map('fits/counts_'+zbin+'_diff.fits', counts_diff[zbin])
print("\tNumber of sources in bin [%.2f,%.2f) is %d" %
(args.zmin, args.zmax, np.sum((counts_a + counts_b) * mask)))
print("...converting to overdensity maps...")
delta_a = hpy.Counts2Delta(counts_a, mask=mask)
delta_b = hpy.Counts2Delta(counts_b, mask=mask)
delta_sum = 0.5 * (delta_a + delta_b)
delta_diff = 0.5 * (delta_a - delta_b)
# hp.write_map('fits/delta_'+zbin+'_sum.fits', delta_sum[zbin])
# hp.write_map('fits/delta_'+zbin+'_diff.fits', delta_diff[zbin])
print("...done...")
# embed()
# exit()
# XC estimator ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
est = cs.Master(mask,
lmin=lmin,
lmax=lmax,
delta_ell=delta_ell,
MASTER=1,
pixwin=True)
print('...extracting gg spectra...')
clGS, err_gg = est.get_spectra(delta_sum, analytic_errors=True)
clGD = est.get_spectra(delta_diff)
gg = clGS - clGD
counts_ = counts_a + counts_b
delta_ = hpy.Counts2Delta(counts_, mask=mask)
gg_, err_gg_ = est.get_spectra(delta_,
nl=GetNlgg(counts_, mask=mask),
pseudo=0,
analytic_errors=True)
print("\t=> Detection at rougly %.2f sigma ") % (np.sum(
(gg[0:] / err_gg[0:])**2)**.5)
print("\t=> Detection at rougly %.2f sigma ") % (np.sum(
(gg_[0:] / err_gg_[0:])**2)**.5)
print('...done...')
# Initializing Cosmo class
cosmo = Cosmo()
cosmo_nl = Cosmo(nonlinear=True)
nz, z, _ = hist(twompz.ZPHOTO, 'knuth', normed=1)
z = 0.5 * (z[1:] + z[:-1])
plt.close()
fig = plt.figure() #figsize=(10, 8))
ax = fig.add_subplot(1, 1, 1)
plt.title(r'$%.2f < z < %.2f$' % (args.zmin, args.zmax))
# z, n_z = GetdNdzNorm(nz=1000)
# clgg = GetAutoSpectrumMagLim( cosmo, 2.21, 0.053, 1.43, bias=1.24, alpha=1., lmax=1000)
# clgg_ph = GetAutoSpectrumMagLim( cosmo_nl, 2.21, 0.053, 1.43, bias=1.24, alpha=1., lmax=1000, sigma_zph=0.03)
# clgg_nl = GetAutoSpectrumMagLim( cosmo_nl, 2.21, 0.053, 1.43, bias=1.24, alpha=1., lmax=1000)
clgg = GetAutoSpectrum(cosmo,
z,
nz, [args.zmin, args.zmax],
b=1.,
alpha=1.,
lmax=500,
sigma_zph=0.015,
i=0)
clgg_nl = GetAutoSpectrum(cosmo_nl,
z,
nz, [args.zmin, args.zmax],
b=1.,
alpha=1.,
lmax=500,
sigma_zph=0.015,
i=0)
# clgg = GetAutoSpectrum(cosmo, z, dndz, [0., 0.08, 0.5], b=1.24, alpha=1., lmax=500, sigma_zph=0.015, i=i)
# clgg_nl = GetAutoSpectrum(cosmo_nl, z, dndz, [0., 0.08, 0.5], b=1.24, alpha=1., lmax=500, sigma_zph=0.015, i=i)
# ax = fig.add_subplot(1,1,i+1)
lab = r'2MPZ $%.1f < K_S < %.1f$' % (K_S_min, K_S_max)
ax.plot(clgg, color='grey', label=r'$b=1$ $\sigma_z=0.015$')
ax.plot(clgg_nl,
color='darkgrey',
ls='--',
label=r'NL $b=1$ $\sigma_z=0.015$')
ax.errorbar(est.lb, gg, yerr=err_gg, fmt='o', capsize=0, ms=5, label=lab)
# ax.errorbar(est.lb+1, gg_[zbin], yerr=err_gg_[zbin], fmt='x', capsize=0, ms=5)#, label=lab)
ax.set_ylabel(r'$C_{\ell}^{gg}$')
ax.set_xlabel(r'Multipole $\ell$')
ax.legend(loc='best')
ax.axhline(ls='--', color='grey')
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_xlim([2., 200])
ax.set_yscale('log')
ax.set_ylim([1e-5, 1e-3])
plt.tight_layout()
plt.savefig('plots/2MPZ_clgg_dl' + str(args.delta_ell) + '_lmin_' +
str(args.lmin) + '_lmax' + str(args.lmax) + '_KS_min_' +
str(args.K_S_min) + '_KS_max_' + str(args.K_S_max) + '_nside' +
str(args.nside) + '_zmin_' + str(args.zmin) + '_zmax' +
str(args.zmax) + '_split.pdf',
bbox_inches='tight')
# plt.show()
# plt.close()
# embed()
np.savetxt(
'spectra/2MPZ_clgg_dl' + str(args.delta_ell) + '_lmin_' +
str(args.lmin) + '_lmax' + str(args.lmax) + '_KS_min_' +
str(args.K_S_min) + '_KS_max_' + str(args.K_S_max) + '_nside' +
str(args.nside) + '_zmin_' + str(args.zmin) + '_zmax' +
str(args.zmax) + '_split.dat', np.c_[est.lb, gg, err_gg])