# AlTau = np.zeros(lmax+1)
# for L in xrange(2,lmax+1):
# for l in xrange(lmax+1):
# for lp in xrange(lmax+1):
# print L, l, lp
# fact = cltt[l]**2/((cltt[l]+nltt[l])+(cltt[lp]+nltt[lp]))
# fact *= (2*l+1)*(2*lp+1)/16./np.pi
# # embed()
# try:
# wig2 = wc.wigner3j(2*L,2*l,2*lp,0.,0.,0.)**2
# except:
# wig2 = 0.
# AlTau[L] += fact * wig2
# return np.nan_to_num(1./AlTau)
if __name__ == '__main__':
Delta_T = 3. # microK-arcmin
fwhm = 0. # arcmin
lmax = 500
cosmo = Cosmo()
cltt = cosmo.cmb_spectra(lmax)[:, 0]
nltt = nl_cmb(Delta_T, fwhm, lmax=lmax)
norm = GetAlTau(cltt, nltt, lmax)
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'
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()
# Params
delta_ell = 10
lmin = 10
lmax = 250
K_S_mins = [0., 12., 13.]
K_S_max = 13.9
zmin = 0.0
zmax = 0.24
bias = 1.
nsims = 500
lbmax = 4
# Initializing Cosmo class
cosmo = Cosmo(Giannantonio15Params, nonlinear=True)
cosmo_lin = Cosmo(Giannantonio15Params)
# To bin theoretical spectra
binner = cs.Binner(lmin=lmin, lmax=lmax, delta_ell=delta_ell)
DGs = []
err_DGs = []
DGs_lin = []
err_DGs_lin = []
for K_S_min in K_S_mins:
# 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)
fname_kg = 'clkg_sims_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]) + '_nsims' + str(nsims) + '.pkl.gz'
clkg_sims[zbin] = pickle.load(gzip.open('spectra/sims/' + fname_kg, 'rb'))
clgg_sims[zbin] = pickle.load(gzip.open('spectra/sims/' + fname_gg, 'rb'))
# assert( clkg_sims[zbins[0]]['lb'] == lb )
# To bin theoretical spectra
binner = cs.Binner(lmin=lmin, lmax=lmax, delta_ell=delta_ell)
# Initializing Cosmo class
# cosmo = Cosmo()
cosmo_nl = Cosmo(Giannantonio15Params, nonlinear=True)
# cosmo_w = Cosmo(params={'w':-0.5})
# 2MPZ dN/dz
nz, z, _ = hist(twompz.ZPHOTO, 'knuth', normed=1, histtype='step')
z = 0.5 * (z[1:] + z[:-1])
plt.close()
# Get theoretical quantities
clkg_slash = {}
clgg_slash = {}
clkg_slash_binned = {}
clgg_slash_binned = {}
for zbin in zbins:
clkg_slash[zbin] = GetCrossSpectrum(cosmo_nl,
import numpy as np
from scipy import interpolate
import os
import matplotlib.pyplot as plt
from cosmojo.universe import Cosmo
from matplotlib.pyplot import cm
import matplotlib.colors as mcolors
from rotation import *
from lensing import *
arcmin2rad = np.pi / 180. / 60.
rad2arcmin = 1./arcmin2rad
cosmo = Cosmo()
cmbspec = cosmo.cmb_spectra(5000)
cmbspec_r = Cosmo({'r':0.1}).cmb_spectra(3000, spec='tensor')
nl0 = nl_cmb(9.8,1.3)
nl1 = nl_cmb(3.4,30)
nl2 = nl_cmb(11.8,3.5)
nl3 = nl_cmb(4.5,1.1)
l = np.arange(nl0.shape[0])
nl_tot =((1./(nl0))+(1./(nl2))+(1./(nl3)))**(-1)
plt.loglog(l,nl0,label='ACTPol')
plt.loglog(l,nl1,label='BICEP3')
plt.loglog(l,nl2,label='SA')
def del_clbb(r):
cmbspec_r = Cosmo({'r': r}).cmb_spectra(1999, spec='tensor')[:, 2]
ell = np.arange(2000)
denom_r = np.sqrt(2. / (2. * ell + 1.)) * (cmbspec_r + ClBBRot[0:2000])
frac_r = (cmbspec_r / denom_r)**2.
return np.sum(frac_r[20:200]) - 6.
)[:, 0]
vec = np.loadtxt(
'/home/student.unimelb.edu.au/brycem1/MagCAMB/bryce/research12/r_test_vecCls.dat'
)[:, 3]
tens_vec = tens + vec
cl_tens_vec = np.nan_to_num(
(tens_vec * 2 * np.pi) / (ell_vec * (ell_vec + 1.)))
arcmin2rad = np.pi / 180. / 60.
rad2arcmin = 1. / arcmin2rad
#Camb initialisation
pars = camb.CAMBparams()
data_camb = camb.get_background(pars)
cosmo = Cosmo()
cmbspec = cosmo.cmb_spectra(3000)
#cmbspec_r = Cosmo({'r':0.1}).cmb_spectra(12000,spec='tensor')[:,2]
clbb_r = Cosmo({'r': 1.}).cmb_spectra(3000, spec='tensor')[:, 2]
clbb_r1 = Cosmo({'r': 0.0042}).cmb_spectra(3000, spec='tensor')[:, 2]
clbb_r2 = Cosmo({'r': 0.01}).cmb_spectra(3000, spec='tensor')[:, 2]
clbb_r3 = Cosmo({'r': 0.1}).cmb_spectra(3000, spec='tensor')[:, 2]
#Set up a new set of parameters for CAMB
pars = camb.CAMBparams()
#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency
pars.set_cosmology(H0=67.5,
ombh2=0.022,
omch2=0.122,
mnu=0.06,
omk=0,
for zbin in zbins:
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)+'_nside'+str(nside)+'_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)+'_nside'+str(nside)+'_zmin_'+str(zbin[0])+'_zmax'+str(zbin[1])+'_split.dat'
lb, clgg[zbin], err_clgg[zbin] = np.loadtxt(file_clgg, unpack=1)
lb, clkg[zbin], err_clkg[zbin] = np.loadtxt(file_clkg, unpack=1)
# fname_gg = 'clgg_sims_dl'+str(delta_ell)+'_lmin_'+str(lmin)+'_lmax'+str(lmax)+'_KS_min_'+str(K_S_min)+'_KS_max_'+str(K_S_max)+'_nside'+str(nside)+'_zmin_'+str(zbins[0][0])+'_zmax'+str(zbins[0][1])+'_nsims'+str(nsims)+'.pkl.gz'
# fname_kg = 'clkg_sims_dl'+str(delta_ell)+'_lmin_'+str(lmin)+'_lmax'+str(lmax)+'_KS_min_'+str(K_S_min)+'_KS_max_'+str(K_S_max)+'_nside'+str(nside)+'_zmin_'+str(zbins[0][0])+'_zmax'+str(zbins[0][1])+'_nsims'+str(nsims)+'.pkl.gz'
# clkg_sims = pickle.load(gzip.open('spectra/sims/'+fname_kg,'rb'))#, protocol=2)
# clgg_sims = pickle.load(gzip.open('spectra/sims/'+fname_gg,'rb'))#, protocol=2)
bias_gg = np.loadtxt('bias_estimator_gg.dat')#clgg_sims['sims'].mean(0)
# bias_kg = clkg_sims['sims'].mean(0)
# Get theoretical quantities
cosmo_nl = Cosmo(Giannantonio15Params, nonlinear=True)
cosmo = Cosmo(Giannantonio15Params)
# 2MPZ dN/dz
# nz, z, _ = hist(twompz.ZSPEC[twompz.ZSPEC>0.],'knuth', normed=1, histtype='step')
nz, z, _ = hist(twompz.ZPHOTO,'knuth', normed=1, histtype='step')
z = 0.5 * (z[1:]+z[:-1])
plt.close()
# embed()
# exit()
binner = cs.Binner(lmin=lmin, lmax=lmax, delta_ell=delta_ell)
clkg_th = {}
clgg_th = {}
def del_Cl_r(del_r, r0=0.1, lmax=3000):
cmbspec_ru = Cosmo({'r': r0 + del_r}).cmb_spectra(lmax, spec='tensor')
cmbspec_rl = Cosmo({'r': r0 - del_r}).cmb_spectra(lmax, spec='tensor')
del_Clr = cmbspec_ru[:, 2] - cmbspec_rl[:, 2]
return del_Clr
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])
from scipy import interpolate
import os
import matplotlib.pyplot as plt
from cosmojo.universe import Cosmo
from matplotlib.pyplot import cm
import matplotlib.colors as mcolors
import rotation as ro
import lensing as le
from IPython import embed
arcmin2rad = np.pi / 180. / 60.
rad2arcmin = 1. / arcmin2rad
cosmo = Cosmo()
cmbspec = cosmo.cmb_spectra(5000)
smspec_r = cosmo.cmb_spectra(3000, 'tensor')
#sigma(r=0)
def sig_r0(fsky, BBpowerspectrum, NlBB, lmin=20, lmax=200):
ell = np.arange(BBpowerspectrum.shape[0])
summand = ((2. * ell[lmin:lmax + 1] + 1) * fsky /
2.) * (BBpowerspectrum[lmin:lmax + 1] / NlBB[lmin:lmax + 1])**2.
return 1.0 / (np.sqrt(np.sum(summand)))
def del_Cl_r(del_r, r0=0.1, lmax=3000):
cmbspec_ru = Cosmo({'r': r0 + del_r}).cmb_spectra(lmax, spec='tensor')
plt.close()
nz_spec, z_spec, _ = hist(twompz.ZSPEC[twompz.ZSPEC>0.],'knuth', normed=1, histtype='step', label=r'Full spec')
# _, _, _ = hist(twompz.ZSPEC[twompz.ZSPEC>0.],'knuth', normed=1, histtype='step', label=r'Full-$z_{\rm s}$')
# _, _, _ = hist(twompz.ZPHOTO[twompz.ZSPEC>0.],'knuth', normed=1, histtype='step', label=r'Cross-$z_{\rm s}$')
z_spec = 0.5 * (z_spec[1:]+z_spec[:-1])
plt.close()
nz_overlap, z_overlap, _ = hist(twompz.ZPHOTO[twompz.ZSPEC>0.],'knuth', normed=1, histtype='step', label=r'overlap photo')
# _, _, _ = hist(twompz.ZSPEC[twompz.ZSPEC>0.],'knuth', normed=1, histtype='step', label=r'Full-$z_{\rm s}$')
# _, _, _ = hist(twompz.ZPHOTO[twompz.ZSPEC>0.],'knuth', normed=1, histtype='step', label=r'Cross-$z_{\rm s}$')
z_overlap = 0.5 * (z_overlap[1:]+z_overlap[:-1])
plt.close()
# CMB lens kernel
lcmb = LensCMB(Cosmo())
# Plot dN/dz
DNDZ = dNdzInterpolation(z, nz, bins=[0.,0.24], sigma_zph=0.015, z_min=0, z_max=1)
DNDZ_spec = dNdzInterpolation(z_spec, nz_spec, bins=[0.,0.24], sigma_zph=0.015, z_min=0, z_max=1)
# DNDZ_overlap = dNdzInterpolation(z_overlap, nz_overlap, bins=[0.,0.24], sigma_zph=0.015, z_min=0, z_max=1)
plt.plot(z, DNDZ.raw_dndz_bin(z,0), label=r'2MPZ $z \le 0.24$', color='#083D77')
nz_spec, z_spec, _ = hist(twompz.ZSPEC[twompz.ZSPEC>0.],'knuth', normed=1, histtype='step', label=r'Spec-z', color='#8C2F39')
# plt.plot(z, DNDZ_spec.raw_dndz_bin(z,0), label=r'2MPZ $z \le 0.24$ spec')
# plt.plot(z, DNDZ_overlap.raw_dndz_bin(z,0), label=r'2MPZ $z \le 0.24$ overlap')
# plt.plot(z, DNDZ.raw_dndz_bin(z,0)/np.max(DNDZ.raw_dndz_bin(z,0)), label='Fiducial')
# for i,zbin in enumerate(zbins):
# DNDZ = dNdzInterpolation(z, nz, bins=[zbin[0],zbin[1]], sigma_zph=0.015, z_min=0, z_max=1)
# # plt.plot(z, DNDZ.raw_dndz_bin(z,0)/np.max(DNDZ.raw_dndz_bin(z,0)), label='Bin-%d' %(i+1))
# plt.plot(z, DNDZ.raw_dndz_bin(z,0), label='Bin-%d' %(i+1))
# plt.plot(z, DNDZ.raw_dndz_bin(z,0), label=r'$%.2f < z < %.2f$' %(zbin[0],zbin[1]))
data = pickle.load(open("../transfer.pkl","rb"))
ell_array = data['ell']
k_array = data['k']
Tl_m1k = data['Tl_m1k']
Tl_p1k = data['Tl_p1k']
T1lk = data['T1lk']
#Loads pre made transfer pickle files from transfer.py for ell>=1000 at del_l=100
data1 = pickle.load(open("../transfer100.pkl","rb"))
ell_array1 = data1['ell']
k_array1 = data1['k']
Tl_m1k1 = data1['Tl_m1k']
Tl_p1k1 = data1['Tl_p1k']
T1lk1 = data1['T1lk']
cosmo = Cosmo()
#cmbspec = cosmo.cmb_spectra(12000)
cmbspec_r = Cosmo({'r':0.1}).cmb_spectra(12000,spec='tensor')[:,2]
#Set up a new set of parameters for CAMB
pars = camb.CAMBparams()
#This function sets up CosmoMC-like settings, with one massive neutrino and helium set using BBN consistency
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.06, omk=0, tau=0.06)
pars.InitPower.set_params(ns=0.965, r=0)
pars.set_for_lmax(12000, lens_potential_accuracy=0)
#calculate results for these parameters
results = camb.get_results(pars)
#get dictionary of CAMB power spectra
powers =results.get_cmb_power_spectra(pars, CMB_unit='muK')
return np.asarray( [GetDg(clkg_sims[i], clgg_sims[i], clkg_th, clgg_th, err_kg=err_kg, err_gg=err_gg, lbmin=lbmin, lbmax=lbmax) for i in xrange(clkg_sims.shape[0])] )
# Params
delta_ell = 20
lmin = 10
lmax = 250
K_S_min = 0. #, 10., 12., 13.]
K_S_max = 13.9
zmin = 0.0
zmax = 0.24
bias = 1.
nsims = 200
lbmax = 4
# Initializing Cosmo class
cosmo = Cosmo()
cosmo_nl = Cosmo(nonlinear=True)
# To bin theoretical spectra
binner = cs.Binner(lmin=lmin, lmax=lmax, delta_ell=delta_ell)
DGs = []
err_DGs = []
# 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...")
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(zmin)+'_zmax'+str(zmax)+'_split.dat'
def clbb2r(r):
return Cosmo({'r': r}).cmb_spectra(220, spec='tensor')[:, 2][20:201]
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])
mask = mask_planck_2015 * mask_2MPZ
# Get the gal number density
cat_tmp = twompz[(twompz.ZPHOTO >= zmin) & (twompz.ZPHOTO < zmax)]
counts = hpy.GetCountsMap(cat_tmp.RA, cat_tmp.DEC, nside, coord='G', rad=True)
counts = hp.ma(counts)
counts.mask = np.logical_not(mask)
ngal = counts.mean()
ngal_sr = ngal / hp.nside2pixarea(nside)
print ngal, ngal_sr
# exit()
# Initializing Cosmo class
cosmo_nl = Cosmo(nonlinear=True)
# 2MPZ dN/dz
# z, n_z = GetdNdzNorm(nz=1000)
nz, z, _ = hist(twompz.ZPHOTO, 'knuth', normed=1, histtype='step')
z = 0.5 * (z[1:] + z[:-1])
# Get theoretical quantities
# clkg_th = GetCrossSpectrumMagLim(cosmo_nl, 2.21, 0.053, 1.43, bias=1., alpha=1., lmax=2000)
# clgg_th = GetAutoSpectrumMagLim( cosmo_nl, 2.21, 0.053, 1.43, bias=1., alpha=1., lmax=2000)
clkg_th = GetCrossSpectrum(cosmo_nl,
z,
nz, [zmin, zmax],
b=1.24,
alpha=1.,
lmax=500,
fname_gg = 'clgg_sims_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])+'_nsims'+str(nsims)+'_fsky.pkl.gz'
fname_kg = 'clkg_sims_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])+'_nsims'+str(nsims)+'_fsky.pkl.gz'
else:
fname_gg = 'clgg_sims_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])+'_nsims'+str(nsims)+'.pkl.gz'
fname_kg = 'clkg_sims_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])+'_nsims'+str(nsims)+'.pkl.gz'
clkg_sims[zbin] = pickle.load(gzip.open('spectra/sims/'+fname_kg,'rb'))
clgg_sims[zbin] = pickle.load(gzip.open('spectra/sims/'+fname_gg,'rb'))
# assert( clkg_sims[zbins[0]]['lb'] == lb )
# To bin theoretical spectra
binner = cs.Binner(lmin=lmin, lmax=lmax, delta_ell=delta_ell)
# Initializing Cosmo class
# cosmo = Cosmo()
cosmo_nl = Cosmo(Giannantonio15Params, nonlinear=True)
# cosmo_w = Cosmo(params={'w':-0.5})
# 2MPZ dN/dz
nz, z, _ = hist(twompz.ZPHOTO,'knuth', normed=1, histtype='step')
z = 0.5 * (z[1:]+z[:-1])
plt.close()
# Get theoretical quantities
clkg_slash = {}
clgg_slash = {}
clkg_slash_binned = {}
clgg_slash_binned = {}
for zbin in zbins:
clkg_slash[zbin] = GetCrossSpectrum(cosmo_nl, z, nz, [zbin[0],zbin[1]], b=bias, alpha=1., lmax=500, sigma_zph=0.015, compute_at_z0=True)
fname_kg = 'clkg_sims_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]) + '_nsims' + str(nsims) + '.pkl.gz'
clkg_sims[zbin] = pickle.load(gzip.open('spectra/sims/' + fname_kg, 'rb'))
clgg_sims[zbin] = pickle.load(gzip.open('spectra/sims/' + fname_gg, 'rb'))
# assert( clkg_sims[zbins[0]]['lb'] == lb )
# To bin theoretical spectra
binner = cs.Binner(lmin=lmin, lmax=lmax, delta_ell=delta_ell)
# Initializing Cosmo class
# cosmo = Cosmo()
cosmo_nl = Cosmo(Giannantonio15Params, nonlinear=True)
# cosmo_w = Cosmo(params={'w':-0.5})
# 2MPZ dN/dz
nz, z, _ = hist(twompz.ZPHOTO, 'knuth', normed=1, histtype='step')
# nz, z, _ = hist(twompz.ZSPEC[twompz.ZSPEC>0.],'knuth', normed=1, histtype='step')
z = 0.5 * (z[1:] + z[:-1])
plt.close()
# Get theoretical quantities
clkg_slash = {}
clgg_slash = {}
clkg_slash_binned = {}
clgg_slash_binned = {}
for zbin in zbins:
from scipy import interpolate
import os
import matplotlib.pyplot as plt
from cosmojo.universe import Cosmo
from matplotlib.pyplot import cm
import matplotlib.colors as mcolors
import rotation as ro
import lensing as le
import numba
from scipy.stats import sem, t
from scipy import mean
from IPython import embed
cosmo = Cosmo()
cmbspec = cosmo.cmb_spectra(400)
cmbspec_r = Cosmo({'r':1.}).cmb_spectra(400,spec='tensor')[:,2]
#exps = [f_sky,noise,beam]
test = [0.5,9.8,1.3]
fsky= 0.5
noice = 9.8
fwhm = 1.3
def delclaa(Nlaa,fsky,dl,ell):
return np.sqrt(2./((2.*ell+1.)*dl*fsky))*(Nlaa)
import os
import matplotlib.pyplot as plt
from cosmojo.universe import Cosmo
from matplotlib.pyplot import cm
import matplotlib.colors as mcolors
import rotation as ro
import lensing as le
import numba
from IPython import embed
arcmin2rad = np.pi / 180. / 60.
rad2arcmin = 1. / arcmin2rad
cosmo = Cosmo()
cmbspec = cosmo.cmb_spectra(5000)
l_rot, ClBBRot = ro.GimmeClBBRot(cmbspec, dl=1, nwanted=3000)
def del_clbb(r):
cmbspec_r = Cosmo({'r': r}).cmb_spectra(1999, spec='tensor')[:, 2]
ell = np.arange(2000)
denom_r = np.sqrt(2. / (2. * ell + 1.)) * (cmbspec_r + ClBBRot[0:2000])
frac_r = (cmbspec_r / denom_r)**2.
return np.sum(frac_r[20:200]) - 6.
embed()
from scipy.optimize import brentq
