def __init__(self,clusterCosmology, \
fwhms=[1.5],rms_noises =[1.], freqs = [150.],lmax=8000,lknee=0.,alpha=1., \
dell=1.,ksz_file='input/ksz_BBPS.txt',ksz_p_file='input/ksz_p_BBPS.txt', \
tsz_cib_file='input/sz_x_cib_template.dat',fg=True):
self.cc = clusterCosmology
if (len(freqs) > 1):
fq_mat = np.matlib.repmat(freqs, len(freqs), 1)
fq_mat_t = np.transpose(np.matlib.repmat(freqs, len(freqs), 1))
else:
fq_mat = freqs
fq_mat_t = freqs
self.fgs = fgNoises(
self.cc.c,
ksz_file=ksz_file,
ksz_p_file=ksz_p_file,
tsz_cib_file=tsz_cib_file,
tsz_battaglia_template_csv="data/sz_template_battaglia.csv")
self.dell = dell
self.evalells = np.arange(2, lmax, self.dell)
self.N_ll_cmb = self.evalells * 0.0
self.W_ll_cmb = np.zeros([len(self.evalells), len(np.array(freqs))])
self.freq = freqs
f_nu_cmb = f_nu_tsz * 0.0 + 1.
for ii in range(len(self.evalells)):
cmb_els = fq_mat * 0.0 + self.cc.cleefunc(self.evalells[ii])
## MAKE POL NOISE
inst_noise = (old_div(
noise_func(self.evalells[ii],
np.array(fwhms),
np.array(rms_noises),
lknee,
alpha,
dimensionless=False), self.cc.c['TCMBmuK']**2.))
nells = np.diag(inst_noise)
totfg = (self.fgs.rad_pol_ps(self.evalells[ii],fq_mat,fq_mat_t) + \
self.fgs.gal_dust_pol(self.evalells[ii],fq_mat,fq_mat_t) + \
self.fgs.gal_sync_pol(self.evalells[ii],fq_mat,fq_mat_t))
N_ll_for_cmb = nells + totfg
N_ll_for_cmb_inv = np.linalg.inv(N_ll_for_cmb)
self.W_ll_cmb[ii,:] = 1./np.dot(np.transpose(f_nu_cmb),np.dot(N_ll_for_cmb_inv,f_nu_cmb)) \
* np.dot(np.transpose(f_nu_cmb),N_ll_for_cmb_inv)
self.N_ll_cmb[ii] = np.dot(
np.transpose(self.W_ll_cmb[ii, :]),
np.dot(N_ll_for_cmb, self.W_ll_cmb[ii, :]))
def __init__(self,shape,wcs,theory,freqs,beams,noises,lknees,alphas,ellmins,ellmaxes):
fgn = fg.fgNoises(cosmology.defaultConstants,ksz_file='/home/msyriac/repos/szar/input/ksz_BBPS.txt',
ksz_p_file='/home/msyriac/repos/szar/input/ksz_p_BBPS.txt',
tsz_cib_file='/home/msyriac/repos/szar/input/sz_x_cib_template.txt',
ksz_battaglia_test_csv=None,
tsz_battaglia_template_csv="/home/msyriac/repos/szar/input/sz_template_battaglia.csv",
rs_template="/home/msyriac/repos/szar/input/fiducial_scalCls_lensed_5_5.txt",
rsx_template="/home/msyriac/repos/szar/input/fiducial_scalCls_lensed_1_5.txt",
components=['tsz','cibp','cibc','radps'],lmax=20000)
self.modlmap = enmap.modlmap(shape,wcs)
modlmap = self.modlmap
self.fgn = fgn
lmax = self.modlmap.max()
ells = np.arange(0,lmax,1)
ps_cmb = theory.lCl('TT',modlmap).reshape((1,1,shape[-2],shape[-1]))
self.ps_cmb = ps_cmb
ps_y = fgn.tsz_template(ells).reshape((1,1,ells.size))*self.fgn.c['A_tsz']*2.*np.pi*np.nan_to_num(1./ells/(ells+1.))
ps_cibp = (fgn.c['A_cibp'] * ((ells/fgn.c['ell0sec'])) ** 2.0 *2.*np.pi*np.nan_to_num(1./ells/(ells+1.))).reshape((1,1,ells.size))
ps_cibc = (fgn.c['A_cibc'] * ((ells/fgn.c['ell0sec'])) ** (2.-fgn.c['n_cib']) * 2.*np.pi*np.nan_to_num(1./ells/(ells+1.))).reshape((1,1,ells.size))
ps_radps = (fgn.c['A_ps'] * ((ells/fgn.c['ell0sec'])) ** 2 * 2.*np.pi*np.nan_to_num(1./ells/(ells+1.))).reshape((1,1,ells.size))
self.cgen = maps.MapGen(shape[-2:],wcs,ps_cmb)
self.tgen = maps.MapGen(shape[-2:],wcs,ps_y)
self.cibpgen = maps.MapGen(shape[-2:],wcs,ps_cibp)
self.cibcgen = maps.MapGen(shape[-2:],wcs,ps_cibc)
self.radpsgen = maps.MapGen(shape[-2:],wcs,ps_radps)
self.shape = shape ; self.wcs = wcs
self.freqs = freqs
self.kbeams = []
self.ngens = []
self.n2ds = []
for ai,nu in enumerate(self.freqs):
self.kbeams.append(maps.gauss_beam(fwhm=beams[ai],ell=self.modlmap))
n2d = cosmology.noise_func(self.modlmap,0,noises[ai],lknee=lknees[ai],alpha=alphas[ai],dimensionless=False,TCMB=2.7255e6)
n2d[modlmap<ellmins[ai]] = 0
n2d[modlmap>ellmaxes[ai]] = 0
n2dmod = n2d.copy()
n2dmod[modlmap>ellmaxes[ai]] = 1e90
n2dmod[modlmap<ellmins[ai]] = 1e90
self.n2ds.append(n2dmod.copy())
ps_noise = n2d.reshape((1,1,shape[-2],shape[-1]))
self.ngens.append(maps.MapGen(shape[-2:],wcs,ps_noise))
self.ellmins = ellmins
self.ellmaxes = ellmaxes
def __init__(self,clusterCosmology, fgs,fwhms=[1.5],rms_noises =[1.], freqs = [150.],lmax=8000,lknee=0.,alpha=1.,dell=1.,v3mode=-1):
#Inputs
#clusterCosmology is a class that contains cosmological parameters and power spectra.
#fgs is a class that contains the functional forms for the foregrounds and constants
#Options
#initial set up for ILC
self.cc = clusterCosmology
#initializing the frequency matrices
if (len(freqs) > 1):
fq_mat = np.matlib.repmat(freqs,len(freqs),1)
fq_mat_t = np.transpose(np.matlib.repmat(freqs,len(freqs),1))
else:
fq_mat = freqs
fq_mat_t = freqs
self.fgs = fgs
self.dell = dell
#set-up ells to evaluate up to lmax
self.evalells = np.arange(2,lmax,self.dell)
self.N_ll_noILC = self.evalells*0.0
self.N_ll_tsz = self.evalells*0.0
self.N_ll_cmb = self.evalells*0.0
self.N_ll_rsx = self.evalells*0.0
self.N_ll_cmb_c_tsz = self.evalells*0.0
self.N_ll_tsz_c_cmb = self.evalells*0.0
self.N_ll_tsz_c_cib = self.evalells*0.0
#Only for SO forecasts, including the SO atmosphere modeling
if v3mode>-1:
print("V3 flag enabled.")
import szar.V3_calc_public as v3
if v3mode <= 2:
vfreqs = v3.Simons_Observatory_V3_LA_bands()
freqs = vfreqs
vbeams = v3.Simons_Observatory_V3_LA_beams()
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
v3ell, N_ell_T_LA, N_ell_P_LA, Map_white_noise_levels = v3.Simons_Observatory_V3_LA_noise(sensitivity_mode=v3mode,f_sky=fsky,ell_max=v3lmax+v3dell,delta_ell=v3dell)
elif v3mode == 3:
vfreqs = v3.AdvACT_bands()
freqs = vfreqs
vbeams = v3.AdvACT_beams()
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
v3ell, N_ell_T_LA, N_ell_P_LA, Map_white_noise_levels = v3.AdvACT_noise(f_sky=fsky,ell_max=v3lmax+v3dell,delta_ell=\
v3dell)
#initializing the weighting functions for the ilc
#thermal SZ weights
self.W_ll_tsz = np.zeros([len(self.evalells),len(np.array(freqs))])
#CMB weights
self.W_ll_cmb = np.zeros([len(self.evalells),len(np.array(freqs))])
#rayleigh scattering cross correlation weights
self.W_ll_rsx = np.zeros([len(self.evalells),len(np.array(freqs))])
#thermal SZ constraining the CIB weights
self.W_ll_tsz_c_cib = np.zeros([len(self.evalells),len(np.array(freqs))])
#thermal SZ constraining the CMB weights
self.W_ll_tsz_c_cmb = np.zeros([len(self.evalells),len(np.array(freqs))])
#CMB constraining the thermal SZ weights
self.W_ll_cmb_c_tsz = np.zeros([len(self.evalells),len(np.array(freqs))])
self.freq = freqs
#frequency functions for
f_nu_tsz = f_nu(self.cc.c,np.array(freqs)) #tSZ
f_nu_cmb = f_nu_tsz*0.0 + 1. #CMB
f_nu_cib = self.fgs.f_nu_cib(np.array(freqs)) #CIB
f_nu_rsx = self.fgs.rs_nu(np.array(freqs)) #Rayleigh Cross
for ii in range(len(self.evalells)):
cmb_els = fq_mat*0.0 + self.cc.clttfunc(self.evalells[ii])
if v3mode < 0:
inst_noise = ( old_div(noise_func(self.evalells[ii],np.array(fwhms),np.array(rms_noises),lknee,alpha,dimensionless=False), self.cc.c['TCMBmuK']**2.))
nells = np.diag(inst_noise)
elif v3mode<=2:
ndiags = []
for ff in range(len(freqs)):
inst_noise = old_div(N_ell_T_LA[ff,ii], self.cc.c['TCMBmuK']**2.)
ndiags.append(inst_noise)
nells = np.diag(np.array(ndiags))
# Adding in atmo. freq-freq correlations
#nells[0,1] = N_ell_T_LA[6,ii]/ self.cc.c['TCMBmuK']**2.
#nells[1,0] = N_ell_T_LA[6,ii]/ self.cc.c['TCMBmuK']**2.
#nells[2,3] = N_ell_T_LA[7,ii]/ self.cc.c['TCMBmuK']**2.
#nells[3,2] = N_ell_T_LA[7,ii]/ self.cc.c['TCMBmuK']**2.
#nells[4,5] = N_ell_T_LA[8,ii]/ self.cc.c['TCMBmuK']**2.
#nells[5,4] = N_ell_T_LA[8,ii]/ self.cc.c['TCMBmuK']**2.
elif v3mode==3:
ndiags = []
for ff in range(len(freqs)):
inst_noise = old_div(N_ell_T_LA[ff,ii], self.cc.c['TCMBmuK']**2.)
ndiags.append(inst_noise)
nells = np.diag(np.array(ndiags))
# Adding in atmo. freq-freq correlations
#nells[0,1] = N_ell_T_LA[5,ii]/ self.cc.c['TCMBmuK']**2.
#nells[1,0] = N_ell_T_LA[5,ii]/ self.cc.c['TCMBmuK']**2.
#nells[2,3] = N_ell_T_LA[6,ii]/ self.cc.c['TCMBmuK']**2.
#nells[3,2] = N_ell_T_LA[6,ii]/ self.cc.c['TCMBmuK']**2.
#nells[3,4] = N_ell_T_LA[7,ii]/ self.cc.c['TCMBmuK']**2.
#nells[4,3] = N_ell_T_LA[7,ii]/ self.cc.c['TCMBmuK']**2.
self.N_ll_noILC[ii] = nells[3,3]
totfg = (self.fgs.rad_pst(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.cib_p(self.evalells[ii],fq_mat,fq_mat_t) +
self.fgs.cib_c(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.tSZ_CIB(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
totfgrs = (self.fgs.rad_ps(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.cib_p(self.evalells[ii],fq_mat,fq_mat_t) +
self.fgs.cib_c(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.rs_auto(self.evalells[ii],fq_mat,fq_mat_t) + \
self.fgs.tSZ_CIB(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii] ) * 2.* np.pi
totfg_cib = (self.fgs.rad_ps(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.tSZ_CIB(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
ksz = fq_mat*0.0 + self.fgs.ksz_temp(self.evalells[ii]) / self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
tsz = self.fgs.tSZ(self.evalells[ii],fq_mat,fq_mat_t) / self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
cib = (self.fgs.cib_p(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.cib_c(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
N_ll_for_tsz = nells + totfg + cmb_els + ksz
N_ll_for_cmb = nells + totfg + tsz + ksz
N_ll_for_rsx = nells + totfg + tsz + ksz + cmb_els
N_ll_for_tsz_c_cmb = nells + totfg
N_ll_for_cmb_c_tsz = N_ll_for_tsz_c_cmb + ksz
N_ll_for_tsz_c_cib = nells + totfg_cib + cmb_els + ksz
N_ll_for_tsz_inv = np.linalg.inv(N_ll_for_tsz)
N_ll_for_cmb_inv = np.linalg.inv(N_ll_for_cmb)
N_ll_for_rsx_inv = np.linalg.inv(N_ll_for_rsx)
N_ll_for_tsz_c_cmb_inv = np.linalg.inv(N_ll_for_tsz_c_cmb)
N_ll_for_cmb_c_tsz_inv = N_ll_for_tsz_c_cmb_inv
N_ll_for_tsz_c_cib_inv = np.linalg.inv(N_ll_for_tsz_c_cib)
self.W_ll_tsz[ii,:]=weightcalculator(f_nu_tsz,N_ll_for_tsz)
self.W_ll_rsx[ii,:]=weightcalculator(f_nu_rsx,N_ll_for_rsx)
self.W_ll_cmb[ii,:]=weightcalculator(f_nu_cmb,N_ll_for_cmb)
self.N_ll_tsz[ii] = np.dot(np.transpose(self.W_ll_tsz[ii,:]),np.dot(N_ll_for_tsz,self.W_ll_tsz[ii,:]))
self.N_ll_cmb[ii] = np.dot(np.transpose(self.W_ll_cmb[ii,:]),np.dot(N_ll_for_cmb,self.W_ll_cmb[ii,:]))
self.N_ll_rsx[ii] = np.dot(np.transpose(self.W_ll_rsx[ii,:]),np.dot(N_ll_for_rsx,self.W_ll_rsx[ii,:]))
self.W_ll_tsz_c_cmb[ii,:]=constweightcalculator(f_nu_cmb,f_nu_tsz,N_ll_for_tsz_c_cmb_inv)
self.W_ll_tsz_c_cib[ii,:]=constweightcalculator(f_nu_cib,f_nu_tsz,N_ll_for_tsz_c_cib_inv)
self.W_ll_cmb_c_tsz[ii,:]=constweightcalculator(f_nu_tsz,f_nu_cmb,N_ll_for_cmb_c_tsz_inv)
self.N_ll_tsz_c_cmb[ii] = np.dot(np.transpose(self.W_ll_tsz_c_cmb[ii,:]),np.dot(N_ll_for_tsz_c_cmb,self.W_ll_tsz_c_cmb[ii,:]))
self.N_ll_cmb_c_tsz[ii] = np.dot(np.transpose(self.W_ll_cmb_c_tsz[ii,:]),np.dot(N_ll_for_cmb_c_tsz,self.W_ll_cmb_c_tsz[ii,:]))
self.N_ll_tsz_c_cib[ii] = np.dot(np.transpose(self.W_ll_tsz_c_cib[ii,:]),np.dot(N_ll_for_tsz_c_cib,self.W_ll_tsz_c_cib[ii,:]))
'''
def noiseFromConfig(Config,
expName,
TCMB=2.7255e6,
beamsOverride=None,
noisesOverride=None,
lkneeTOverride=None,
lkneePOverride=None,
alphaTOverride=None,
alphaPOverride=None,
tellminOverride=None,
pellminOverride=None,
tellmaxOverride=None,
pellmaxOverride=None):
tellmin, tellmax = list_from_config(Config, expName, 'tellrange')
if tellminOverride is not None: tellmin = tellminOverride
if tellmaxOverride is not None: tellmax = tellmaxOverride
pellmin, pellmax = list_from_config(Config, expName, 'pellrange')
if pellminOverride is not None: pellmin = pellminOverride
if pellmaxOverride is not None: pellmax = pellmaxOverride
if beamsOverride is not None:
beams = beamsOverride
else:
beams = list_from_config(Config, expName, 'beams')
if noisesOverride is not None:
noises = noisesOverride
else:
noises = list_from_config(Config, expName, 'noises')
lkneeT, lkneeP = list_from_config(Config, expName, 'lknee')
alphaT, alphaP = list_from_config(Config, expName, 'alpha')
if lkneeTOverride is not None: lkneeT = lkneeTOverride
if lkneePOverride is not None: lkneeP = lkneePOverride
if alphaTOverride is not None: alphaT = alphaTOverride
if alphaPOverride is not None: alphaP = alphaPOverride
invnTTs = 0.
invnEEs = 0.
for beam, noise in zip(beams, noises):
invnTTs += 1. / cosmo.noise_func(np.arange(tellmin, tellmax),
beam,
noise,
lknee=lkneeT,
alpha=alphaT,
TCMB=TCMB,
dimensionless=True)
invnEEs += 1. / cosmo.noise_func(np.arange(pellmin, pellmax),
beam,
noise * np.sqrt(2.),
lknee=lkneeP,
alpha=alphaP,
TCMB=TCMB,
dimensionless=True)
fnTT = interp1d(np.arange(tellmin, tellmax),
1. / invnTTs,
bounds_error=False,
fill_value=np.inf)
fnEE = interp1d(np.arange(pellmin, pellmax),
1. / invnEEs,
bounds_error=False,
fill_value=np.inf)
return fnTT, fnEE
def __init__(self,
clusterCosmology,
fgs,
fwhms=[1.5],
rms_noises=[1.],
freqs=[150.],
lmax=8000,
lknee=0.,
alpha=1.,
dell=1.,
v3mode=-1):
#ksz_file='input/ksz_BBPS.txt',ksz_p_file='input/ksz_p_BBPS.txt', \
#tsz_cib_file='input/sz_x_cib_template.dat',fg=True):
self.cc = clusterCosmology
if (len(freqs) > 1):
fq_mat = np.matlib.repmat(freqs, len(freqs), 1)
fq_mat_t = np.transpose(np.matlib.repmat(freqs, len(freqs), 1))
else:
fq_mat = freqs
fq_mat_t = freqs
self.fgs = fgs #fgNoises(self.cc.c,ksz_file=ksz_file,ksz_p_file=ksz_p_file,tsz_cib_file=tsz_cib_file,tsz_battaglia_template_csv="data/sz_template_battaglia.csv")
self.dell = dell
self.evalells = np.arange(2, lmax, self.dell)
self.N_ll_noILC = self.evalells * 0.0
self.N_ll_tsz = self.evalells * 0.0
self.N_ll_cmb = self.evalells * 0.0
self.N_ll_rsx = self.evalells * 0.0
self.N_ll_cmb_c_tsz = self.evalells * 0.0
self.N_ll_tsz_c_cmb = self.evalells * 0.0
self.N_ll_tsz_c_cib = self.evalells * 0.0
if v3mode > -1:
print("V3 flag enabled.")
import szar.V3_calc_public as v3
if v3mode <= 2:
vfreqs = v3.Simons_Observatory_V3_LA_bands()
freqs = vfreqs
vbeams = v3.Simons_Observatory_V3_LA_beams()
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
v3ell, N_ell_T_LA, N_ell_P_LA, Map_white_noise_levels = v3.Simons_Observatory_V3_LA_noise(
sensitivity_mode=v3mode,
f_sky=fsky,
ell_max=v3lmax + v3dell,
delta_ell=v3dell)
elif v3mode == 3:
vfreqs = v3.AdvACT_bands()
freqs = vfreqs
vbeams = v3.AdvACT_beams()
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
v3ell, N_ell_T_LA, N_ell_P_LA, Map_white_noise_levels = v3.AdvACT_noise(f_sky=fsky,ell_max=v3lmax+v3dell,delta_ell=\
v3dell)
self.W_ll_tsz = np.zeros([len(self.evalells), len(np.array(freqs))])
self.W_ll_cmb = np.zeros([len(self.evalells), len(np.array(freqs))])
self.W_ll_rsx = np.zeros([len(self.evalells), len(np.array(freqs))])
self.W_ll_tsz_c_cib = np.zeros(
[len(self.evalells), len(np.array(freqs))])
self.W_ll_tsz_c_cmb = np.zeros(
[len(self.evalells), len(np.array(freqs))])
self.W_ll_cmb_c_tsz = np.zeros(
[len(self.evalells), len(np.array(freqs))])
self.freq = freqs
f_nu_tsz = f_nu(self.cc.c, np.array(freqs))
f_nu_cmb = f_nu_tsz * 0.0 + 1.
f_nu_cib = self.fgs.f_nu_cib(np.array(freqs))
f_nu_rsx = self.fgs.rs_nu(np.array(freqs))
for ii in range(len(self.evalells)):
cmb_els = fq_mat * 0.0 + self.cc.clttfunc(self.evalells[ii])
if v3mode < 0:
inst_noise = (old_div(
noise_func(self.evalells[ii],
np.array(fwhms),
np.array(rms_noises),
lknee,
alpha,
dimensionless=False), self.cc.c['TCMBmuK']**2.))
nells = np.diag(inst_noise)
elif v3mode <= 2:
ndiags = []
for ff in range(len(freqs)):
inst_noise = old_div(N_ell_T_LA[ff, ii],
self.cc.c['TCMBmuK']**2.)
ndiags.append(inst_noise)
nells = np.diag(np.array(ndiags))
# Adding in atmo. freq-freq correlations
#nells[0,1] = N_ell_T_LA[6,ii]/ self.cc.c['TCMBmuK']**2.
#nells[1,0] = N_ell_T_LA[6,ii]/ self.cc.c['TCMBmuK']**2.
#nells[2,3] = N_ell_T_LA[7,ii]/ self.cc.c['TCMBmuK']**2.
#nells[3,2] = N_ell_T_LA[7,ii]/ self.cc.c['TCMBmuK']**2.
#nells[4,5] = N_ell_T_LA[8,ii]/ self.cc.c['TCMBmuK']**2.
#nells[5,4] = N_ell_T_LA[8,ii]/ self.cc.c['TCMBmuK']**2.
elif v3mode == 3:
ndiags = []
for ff in range(len(freqs)):
inst_noise = old_div(N_ell_T_LA[ff, ii],
self.cc.c['TCMBmuK']**2.)
ndiags.append(inst_noise)
nells = np.diag(np.array(ndiags))
# Adding in atmo. freq-freq correlations
#nells[0,1] = N_ell_T_LA[5,ii]/ self.cc.c['TCMBmuK']**2.
#nells[1,0] = N_ell_T_LA[5,ii]/ self.cc.c['TCMBmuK']**2.
#nells[2,3] = N_ell_T_LA[6,ii]/ self.cc.c['TCMBmuK']**2.
#nells[3,2] = N_ell_T_LA[6,ii]/ self.cc.c['TCMBmuK']**2.
#nells[3,4] = N_ell_T_LA[7,ii]/ self.cc.c['TCMBmuK']**2.
#nells[4,3] = N_ell_T_LA[7,ii]/ self.cc.c['TCMBmuK']**2.
self.N_ll_noILC[ii] = nells[3, 3]
totfg = (self.fgs.rad_ps(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.cib_p(self.evalells[ii],fq_mat,fq_mat_t) +
self.fgs.cib_c(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.tSZ_CIB(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
totfgrs = (self.fgs.rad_ps(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.cib_p(self.evalells[ii],fq_mat,fq_mat_t) +
self.fgs.cib_c(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.rs_auto(self.evalells[ii],fq_mat,fq_mat_t) + \
self.fgs.tSZ_CIB(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii] ) * 2.* np.pi
totfg_cib = (self.fgs.rad_ps(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.tSZ_CIB(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
ksz = fq_mat * 0.0 + self.fgs.ksz_temp(
self.evalells[ii]) / self.cc.c['TCMBmuK']**2. / (
(self.evalells[ii] + 1.) * self.evalells[ii]) * 2. * np.pi
tsz = self.fgs.tSZ(
self.evalells[ii], fq_mat,
fq_mat_t) / self.cc.c['TCMBmuK']**2. / (
(self.evalells[ii] + 1.) * self.evalells[ii]) * 2. * np.pi
cib = (self.fgs.cib_p(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.cib_c(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
N_ll_for_tsz = nells + totfg + cmb_els + ksz
N_ll_for_cmb = nells + totfg + tsz + ksz
N_ll_for_rsx = nells + totfg + tsz + ksz + cmb_els
N_ll_for_tsz_c_cmb = nells + totfg
N_ll_for_cmb_c_tsz = N_ll_for_tsz_c_cmb + ksz
N_ll_for_tsz_c_cib = nells + totfg_cib + cmb_els + ksz
N_ll_for_tsz_inv = np.linalg.inv(N_ll_for_tsz)
N_ll_for_cmb_inv = np.linalg.inv(N_ll_for_cmb)
N_ll_for_rsx_inv = np.linalg.inv(N_ll_for_rsx)
N_ll_for_tsz_c_cmb_inv = np.linalg.inv(N_ll_for_tsz_c_cmb)
N_ll_for_cmb_c_tsz_inv = N_ll_for_tsz_c_cmb_inv
N_ll_for_tsz_c_cib_inv = np.linalg.inv(N_ll_for_tsz_c_cib)
self.W_ll_tsz[ii,:] = 1./np.dot(np.transpose(f_nu_tsz),np.dot(N_ll_for_tsz_inv,f_nu_tsz)) \
* np.dot(np.transpose(f_nu_tsz),N_ll_for_tsz_inv)
self.W_ll_cmb[ii,:] = 1./np.dot(np.transpose(f_nu_cmb),np.dot(N_ll_for_cmb_inv,f_nu_cmb)) \
* np.dot(np.transpose(f_nu_cmb),N_ll_for_cmb_inv)
self.W_ll_rsx[ii,:] = 1./np.dot(np.transpose(f_nu_rsx),np.dot(N_ll_for_rsx_inv,f_nu_rsx)) \
* np.dot(np.transpose(f_nu_rsx),N_ll_for_rsx_inv)
self.N_ll_tsz[ii] = np.dot(
np.transpose(self.W_ll_tsz[ii, :]),
np.dot(N_ll_for_tsz, self.W_ll_tsz[ii, :]))
self.N_ll_cmb[ii] = np.dot(
np.transpose(self.W_ll_cmb[ii, :]),
np.dot(N_ll_for_cmb, self.W_ll_cmb[ii, :]))
self.N_ll_rsx[ii] = np.dot(
np.transpose(self.W_ll_rsx[ii, :]),
np.dot(N_ll_for_rsx, self.W_ll_rsx[ii, :]))
self.W_ll_tsz_c_cmb[ii,:] = old_div((np.dot(np.transpose(f_nu_cmb),np.dot(N_ll_for_tsz_c_cmb_inv,f_nu_cmb)) \
* np.dot(np.transpose(f_nu_tsz),N_ll_for_tsz_c_cmb_inv) \
- np.dot(np.transpose(f_nu_tsz),np.dot(N_ll_for_tsz_c_cmb_inv,f_nu_cmb)) \
* np.dot(np.transpose(f_nu_cmb),N_ll_for_tsz_c_cmb_inv)), \
(np.dot(np.transpose(f_nu_cmb),np.dot(N_ll_for_tsz_c_cmb_inv,f_nu_cmb)) \
* np.dot(np.transpose(f_nu_tsz),np.dot(N_ll_for_tsz_c_cmb_inv,f_nu_tsz)) \
- (np.dot(np.transpose(f_nu_tsz),np.dot(N_ll_for_tsz_c_cmb_inv,f_nu_cmb)))**2))
self.W_ll_tsz_c_cib[ii,:] = old_div((np.dot(np.transpose(f_nu_cib),np.dot(N_ll_for_tsz_c_cib_inv,f_nu_cib)) \
* np.dot(np.transpose(f_nu_tsz),N_ll_for_tsz_c_cib_inv) \
- np.dot(np.transpose(f_nu_tsz),np.dot(N_ll_for_tsz_c_cib_inv,f_nu_cib)) \
* np.dot(np.transpose(f_nu_cib),N_ll_for_tsz_c_cib_inv)), \
(np.dot(np.transpose(f_nu_cib),np.dot(N_ll_for_tsz_c_cib_inv,f_nu_cib)) \
* np.dot(np.transpose(f_nu_tsz),np.dot(N_ll_for_tsz_c_cib_inv,f_nu_tsz)) \
- (np.dot(np.transpose(f_nu_tsz),np.dot(N_ll_for_tsz_c_cib_inv,f_nu_cib)))**2))
self.W_ll_cmb_c_tsz[ii,:] = old_div((np.dot(np.transpose(f_nu_tsz),np.dot(N_ll_for_cmb_c_tsz_inv,f_nu_tsz)) \
* np.dot(np.transpose(f_nu_cmb),N_ll_for_cmb_c_tsz_inv) \
- np.dot(np.transpose(f_nu_cmb),np.dot(N_ll_for_cmb_c_tsz_inv,f_nu_tsz)) \
* np.dot(np.transpose(f_nu_tsz),N_ll_for_cmb_c_tsz_inv)), \
(np.dot(np.transpose(f_nu_cmb),np.dot(N_ll_for_cmb_c_tsz_inv,f_nu_cmb)) \
* np.dot(np.transpose(f_nu_tsz),np.dot(N_ll_for_cmb_c_tsz_inv,f_nu_tsz)) \
- (np.dot(np.transpose(f_nu_cmb),np.dot(N_ll_for_cmb_c_tsz_inv,f_nu_tsz)))**2))
self.N_ll_tsz_c_cmb[ii] = np.dot(
np.transpose(self.W_ll_tsz_c_cmb[ii, :]),
np.dot(N_ll_for_tsz_c_cmb, self.W_ll_tsz_c_cmb[ii, :]))
self.N_ll_cmb_c_tsz[ii] = np.dot(
np.transpose(self.W_ll_cmb_c_tsz[ii, :]),
np.dot(N_ll_for_cmb_c_tsz, self.W_ll_cmb_c_tsz[ii, :]))
self.N_ll_tsz_c_cib[ii] = np.dot(
np.transpose(self.W_ll_tsz_c_cib[ii, :]),
np.dot(N_ll_for_tsz_c_cib, self.W_ll_tsz_c_cib[ii, :]))
def __init__(self,clusterCosmology,clusterDict, \
fwhms=[1.5],rms_noises =[1.], freqs = [150.],lmax=8000,lknee=0.,alpha=1., \
dell=10,pmaxN=5,numps=1000,nMax=1, \
ymin=1.e-14,ymax=4.42e-9,dlnY = 0.1, qmin=5., \
ksz_file=root_dir+'input/ksz_BBPS.txt',ksz_p_file=root_dir+'input/ksz_p_BBPS.txt', \
tsz_cib_file=root_dir+'input/sz_x_cib_template.txt',fg=True,tsz_cib=False,
tsz_battaglia_template_csv=root_dir+"input/sz_template_battaglia.csv",v3mode=-1,fsky=None):
self.cc = clusterCosmology
self.P0 = clusterDict['P0']
self.xc = clusterDict['xc']
self.al = clusterDict['al']
self.gm = clusterDict['gm']
self.bt = clusterDict['bt']
self.scaling = self.cc.paramDict
self.qmin = qmin
lnYmin = np.log(ymin)
lnYmax = np.log(ymax)
self.lnY = np.arange(lnYmin, lnYmax, dlnY)
fgs = fgNoises(self.cc.c,
ksz_file=ksz_file,
ksz_p_file=ksz_p_file,
tsz_cib_file=tsz_cib_file,
tsz_battaglia_template_csv=tsz_battaglia_template_csv)
self.dell = dell
self.nlinv = 0.
self.nlinv_cmb = 0.
self.nlinv_nofg = 0.
self.nlinv_cmb_nofg = 0.
self.evalells = np.arange(2, lmax, self.dell)
if v3mode > -1:
print("V3 flag enabled.")
import szar.V3_calc_public as v3
if v3mode <= 2:
import szar.so_noise_lat_v3_1_CAND as v3_1
lat = v3_1.SOLatV3point1(v3mode, el=50.)
vfreqs = lat.get_bands() # v3.Simons_Observatory_V3_LA_bands()
print("Simons Obs")
print("Replacing ", freqs, " with ", vfreqs)
N_bands = len(vfreqs)
freqs = vfreqs
vbeams = lat.get_beams() #v3.Simons_Observatory_V3_LA_beams()
print("Replacing ", fwhms, " with ", vbeams)
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
print("Using ", fsky, " for fsky")
v3ell, N_ell_T_LA_full, N_ell_P_LA = lat.get_noise_curves(
fsky,
v3lmax + v3dell,
v3dell,
full_covar=True,
deconv_beam=True)
N_ell_T_LA = np.diagonal(N_ell_T_LA_full).T
Map_white_noise_levels = lat.get_white_noise(fsky)**.5
#v3ell, N_ell_T_LA, N_ell_P_LA, Map_white_noise_levels = v3.Simons_Observatory_V3_LA_noise(sensitivity_mode=v3mode,f_sky=fsky,ell_max=v3lmax+v3dell,delta_ell=v3dell)
elif v3mode == 3:
vfreqs = v3.AdvACT_bands()
print("AdvACT")
print("Replacing ", freqs, " with ", vfreqs)
freqs = vfreqs
vbeams = v3.AdvACT_beams()
print("Replacing ", fwhms, " with ", vbeams)
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
print("Using ", fsky, " for fsky")
v3ell, N_ell_T_LA, N_ell_P_LA, Map_white_noise_levels = v3.AdvACT_noise(
f_sky=fsky, ell_max=v3lmax + v3dell, delta_ell=v3dell)
elif v3mode == 4:
import szar.noise_model_190604d_public as s4
#mode = 2
#ncalc = s4.S4LatV1(mode, N_tels=2)
#vfreqs = ncalc.get_bands()
print("S4")
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
v3ell = np.arange(2, v3lmax + 1, v3dell)
#print (v3ell, self.evalells)
info, vfreqs, Nmatrix = s4.get_model('hires_deepwide',
'TT',
v3ell,
gal_cut=10,
deconv_beam=True)
print("Replacing ", freqs, " with ", vfreqs)
print("Using ", fsky, " for fsky")
freqs = vfreqs
#vbeams = ncalc.get_beams()
#print("Replacing ",fwhms, " with ", vbeams)
fwhms = freqs * 1. #not used
N_ell_T_LA_full = Nmatrix
N_ell_T_LA = np.diagonal(N_ell_T_LA_full).T
print(N_ell_T_LA.shape)
elif v3mode == 5:
import szar.lat_noise_190528_w350ds4 as ccatp
tubes = (0, 0, 0, 2, 2, 1)
lat = ccatp.CcatLatv2(v3mode,
el=50.,
survey_years=4000 / 24. / 365.24,
survey_efficiency=1.0,
N_tubes=tubes)
vfreqs = lat.get_bands() # v3.Simons_Observatory_V3_LA_bands()
print("CCATP")
print("Replacing ", freqs, " with ", vfreqs)
N_bands = len(vfreqs)
freqs = vfreqs
vbeams = lat.get_beams() #v3.Simons_Observatory_V3_LA_beams()
print("Replacing ", fwhms, " with ", vbeams)
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
print("Using ", fsky, " for fsky")
v3ell, N_ell_T_LA_full, N_ell_P_LA = lat.get_noise_curves(
fsky,
v3lmax + v3dell,
v3dell,
full_covar=True,
deconv_beam=True)
N_ell_T_LA = np.diagonal(N_ell_T_LA_full).T
Map_white_noise_levels = lat.get_white_noise(fsky)**.5
if v3mode >= 7:
usemode = v3mode - 6
print("V3mode ", usemode)
vfreqs = v3.Simons_Observatory_V3_LA_bands()
print("Simons Obs")
print("Replacing ", freqs, " with ", vfreqs)
freqs = vfreqs
vbeams = v3.Simons_Observatory_V3_LA_beams()
print("Replacing ", fwhms, " with ", vbeams)
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
print("Using ", fsky, " for fsky")
v3ell, N_ell_T_LA, N_ell_P_LA, Map_white_noise_levels = v3.Simons_Observatory_V3_LA_noise(
sensitivity_mode=usemode,
f_sky=fsky,
ell_max=v3lmax + v3dell,
delta_ell=v3dell)
assert np.all(v3ell == self.evalells)
# pl = io.Plotter(yscale='log',xlabel='l',ylabel='D_l')
for ii, (freq, fwhm, noise) in enumerate(zip(freqs, fwhms,
rms_noises)):
freq_fac = (f_nu(self.cc.c, freq))**2
if v3mode > -1:
inst_noise = N_ell_T_LA[ii] / self.cc.c['TCMBmuK']**2.
else:
inst_noise = (noise_func(self.evalells,
fwhm,
noise,
lknee,
alpha,
dimensionless=False) /
self.cc.c['TCMBmuK']**2.)
# pl.add(self.evalells,inst_noise*self.evalells**2.,color="C"+str(ii))
# pl.add(self.evalells,N_ell_T_LA[ii]*self.evalells**2./ self.cc.c['TCMBmuK']**2.,color="C"+str(ii),ls="--")
nells = self.cc.clttfunc(self.evalells) + inst_noise
self.nlinv_nofg += old_div((freq_fac), nells)
self.nlinv_cmb_nofg += (old_div(1., inst_noise))
totfg = (fgs.rad_ps(self.evalells,freq,freq) + fgs.cib_p(self.evalells,freq,freq) + \
fgs.cib_c(self.evalells,freq,freq) + fgs.ksz_temp(self.evalells)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells+1.)*self.evalells) * 2.* np.pi
nells += totfg
if (tsz_cib):
tszcib = fgs.tSZ_CIB(self.evalells,freq,freq) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells+1.)*self.evalells) * 2.* np.pi
nells += tszcib
self.nlinv += old_div((freq_fac), nells)
self.nlinv_cmb += (old_div(1., (inst_noise + totfg)))
# pl.add(self.evalells,self.cc.clttfunc(self.evalells)*self.evalells**2.,color='k',lw=3)
# pl.done(io.dout_dir+"v3comp.png")
self.nl_old = (1. / self.nlinv)
self.nl_cmb = (1. / self.nlinv_cmb)
self.nl_nofg = (1. / self.nlinv_nofg)
self.nl_cmb_nofg = (1. / self.nlinv_cmb_nofg)
f_nu_tsz = f_nu(self.cc.c, np.array(freqs))
if (len(freqs) > 1):
fq_mat = repmat(freqs, len(freqs), 1)
fq_mat_t = np.transpose(repmat(freqs, len(freqs), 1))
else:
fq_mat = np.array(freqs)
fq_mat_t = np.array(freqs)
self.nl = self.evalells * 0.0
for ii in range(len(self.evalells)):
cmb_els = fq_mat * 0.0 + self.cc.clttfunc(self.evalells[ii])
if v3mode < 0:
inst_noise = (old_div(
noise_func(self.evalells[ii],
np.array(fwhms),
np.array(rms_noises),
lknee,
alpha,
dimensionless=False), self.cc.c['TCMBmuK']**2.))
nells = np.diag(inst_noise)
elif v3mode <= 2:
nells = N_ell_T_LA_full[:, :, ii] / self.cc.c['TCMBmuK']**2.
#corr_pairs = [(0,1),(2,3),(4,5)]
#N_ell_LA_T = N_ell_LA_T_full[range(N_bands),range(N_bands)]
#N_ell_LA_Tx = [N_ell_LA_T_full[i,j] for i,j in corr_pairs]
#N_ell_LA_P = N_ell_LA_P_full[range(N_bands),range(N_bands)]
#N_ell_LA_Px = [N_ell_LA_P_full[i,j] for i,j in corr_pairs]
#nells = N_ell_LA_Tx
#ndiags = []
#for ff in range(len(freqs)):
# inst_noise = old_div(N_ell_T_LA[ff,ii], self.cc.c['TCMBmuK']**2.)
# ndiags.append(inst_noise)
#nells = np.diag(np.array(ndiags))
## Adding in atmo. freq-freq correlations
#nells[0,1] = old_div(N_ell_T_LA[6,ii], self.cc.c['TCMBmuK']**2.)
#nells[1,0] = old_div(N_ell_T_LA[6,ii], self.cc.c['TCMBmuK']**2.)
#nells[2,3] = old_div(N_ell_T_LA[7,ii], self.cc.c['TCMBmuK']**2.)
#nells[3,2] = old_div(N_ell_T_LA[7,ii], self.cc.c['TCMBmuK']**2.)
#nells[4,5] = old_div(N_ell_T_LA[8,ii], self.cc.c['TCMBmuK']**2.)
#nells[5,4] = old_div(N_ell_T_LA[8,ii], self.cc.c['TCMBmuK']**2.)
elif v3mode == 3:
ndiags = []
for ff in range(len(freqs)):
inst_noise = old_div(N_ell_T_LA[ff, ii],
self.cc.c['TCMBmuK']**2.)
ndiags.append(inst_noise)
nells = np.diag(np.array(ndiags))
# Adding in atmo. freq-freq correlations
nells[0, 1] = old_div(N_ell_T_LA[5, ii],
self.cc.c['TCMBmuK']**2.)
nells[1, 0] = old_div(N_ell_T_LA[5, ii],
self.cc.c['TCMBmuK']**2.)
nells[2, 3] = old_div(N_ell_T_LA[6, ii],
self.cc.c['TCMBmuK']**2.)
nells[3, 2] = old_div(N_ell_T_LA[6, ii],
self.cc.c['TCMBmuK']**2.)
nells[3, 4] = old_div(N_ell_T_LA[7, ii],
self.cc.c['TCMBmuK']**2.)
nells[4, 3] = old_div(N_ell_T_LA[7, ii],
self.cc.c['TCMBmuK']**2.)
elif v3mode == 4:
nells = N_ell_T_LA_full[:, :, ii] / self.cc.c['TCMBmuK']**2.
elif v3mode == 5:
nells = N_ell_T_LA_full[:, :, ii] / self.cc.c['TCMBmuK']**2.
elif v3mode >= 7:
ndiags = []
for ff in range(len(freqs)):
inst_noise = old_div(N_ell_T_LA[ff, ii],
self.cc.c['TCMBmuK']**2.)
ndiags.append(inst_noise)
nells = np.diag(np.array(ndiags))
# Adding in atmo. freq-freq correlations
nells[0, 1] = old_div(N_ell_T_LA[6, ii],
self.cc.c['TCMBmuK']**2.)
nells[1, 0] = old_div(N_ell_T_LA[6, ii],
self.cc.c['TCMBmuK']**2.)
nells[2, 3] = old_div(N_ell_T_LA[7, ii],
self.cc.c['TCMBmuK']**2.)
nells[3, 2] = old_div(N_ell_T_LA[7, ii],
self.cc.c['TCMBmuK']**2.)
nells[4, 5] = old_div(N_ell_T_LA[8, ii],
self.cc.c['TCMBmuK']**2.)
nells[5, 4] = old_div(N_ell_T_LA[8, ii],
self.cc.c['TCMBmuK']**2.)
totfg = (fgs.rad_ps(self.evalells[ii],fq_mat,fq_mat_t) + fgs.cib_p(self.evalells[ii],fq_mat,fq_mat_t)
+ fgs.cib_c(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
if (tsz_cib):
totfg += fgs.tSZ_CIB(self.evalells[ii], fq_mat,
fq_mat_t) / self.cc.c['TCMBmuK']**2. / (
(self.evalells[ii] + 1.) *
self.evalells[ii]) * 2. * np.pi
totfg += fgs.tSZ(
self.evalells[ii], fq_mat, fq_mat_t
) / self.cc.c['TCMBmuK']**2. / (
(self.evalells[ii] + 1.) * self.evalells[ii]
) * 2. * np.pi / 2. # factor of two accounts for resolved halos
ksz = fq_mat * 0.0 + fgs.ksz_temp(
self.evalells[ii]) / self.cc.c['TCMBmuK']**2. / (
(self.evalells[ii] + 1.) * self.evalells[ii]) * 2. * np.pi
nells += totfg + cmb_els + ksz
self.nl[ii] = old_div(
1., (np.dot(np.transpose(f_nu_tsz),
np.dot(np.linalg.inv(nells), f_nu_tsz))))
# from orphics.io import Plotter
# pl = Plotter(yscale='log',xlabel='l',ylabel='D_l')
# pl.add(self.evalells,self.nl*self.evalells**2.)
# ells = np.arange(2,3000,1)
# pl.add(ells,self.cc.clttfunc(ells)*ells**2.)
# pl.done("nltt.png")
# sys.exit()
self.fg = fg
c = self.xc
alpha = self.al
beta = self.bt
gamma = self.gm
p = lambda x: old_div(1., (((c * x)**gamma) * ((1. + (
(c * x)**alpha))**(old_div((beta - gamma), alpha)))))
pmaxN = pmaxN
numps = numps
pzrange = np.linspace(-pmaxN, pmaxN, numps)
self.g = lambda x: np.trapz(p(np.sqrt(pzrange**2. + x**2.)), pzrange,
np.diff(pzrange))
self.gxrange = np.linspace(0., nMax, numps)
self.gint = np.array([self.g(x) for x in self.gxrange])
self.gnorm_pre = np.trapz(self.gxrange * self.gint, self.gxrange)
def __init__(self,
clusterCosmology,
fgs,
fwhms=[1.5],
rms_noises=[1.],
freqs=[150.],
lmax=8000,
lknee=0.,
alpha=1.,
dell=1.,
v3mode=-1,
fsky=None):
#Inputs
#clusterCosmology is a class that contains cosmological parameters and power spectra.
#fgs is a class that contains the functional forms for the foregrounds and constants
#Options
#initial set up for ILC
self.cc = clusterCosmology
#initializing the frequency matrices
self.fgs = fgs
self.dell = dell
#set-up ells to evaluate up to lmax
self.evalells = np.arange(2, lmax, self.dell)
self.N_ll_tsz = self.evalells * 0.0
self.N_ll_cmb = self.evalells * 0.0
self.N_ll_rsx = self.evalells * 0.0
self.N_ll_rsxEE = self.evalells * 0.0
self.N_ll_rsx_NoFG = self.evalells * 0.0
self.N_ll_cmb_c_tsz = self.evalells * 0.0
self.N_ll_tsz_c_cmb = self.evalells * 0.0
self.N_ll_tsz_c_cib = self.evalells * 0.0
#Only for SO forecasts, including the SO atmosphere modeling
if v3mode > -1:
print("V3 flag enabled.")
import szar.V3_calc_public as v3
import szar.so_noise_lat_v3_1_CAND as v3_1
if v3mode <= 2:
lat = v3_1.SOLatV3point1(v3mode, el=50.)
vfreqs = lat.get_bands() # v3.Simons_Observatory_V3_LA_bands()
print("Simons Obs")
print("Replacing ", freqs, " with ", vfreqs)
N_bands = len(vfreqs)
freqs = vfreqs
vbeams = lat.get_beams() #v3.Simons_Observatory_V3_LA_beams()
print("Replacing ", fwhms, " with ", vbeams)
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
print("Using ", fsky, " for fsky")
v3ell, N_ell_T_LA_full, N_ell_P_LA = lat.get_noise_curves(
fsky,
v3lmax + v3dell,
v3dell,
full_covar=True,
deconv_beam=True)
N_ell_T_LA = np.diagonal(N_ell_T_LA_full).T
Map_white_noise_levels = lat.get_white_noise(fsky)**.5
#if v3mode <= 2:
# vfreqs = v3.Simons_Observatory_V3_LA_bands()
# freqs = vfreqs
# vbeams = v3.Simons_Observatory_V3_LA_beams()
# fwhms = vbeams
# v3lmax = self.evalells.max()
# v3dell = np.diff(self.evalells)[0]
# v3ell, N_ell_T_LA, N_ell_P_LA, Map_white_noise_levels = v3.Simons_Observatory_V3_LA_noise(sensitivity_mode=v3mode,f_sky=fsky,ell_max=v3lmax+v3dell,delta_ell=v3dell)
elif v3mode == 3:
vfreqs = v3.AdvACT_bands()
freqs = vfreqs
vbeams = v3.AdvACT_beams()
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
v3ell, N_ell_T_LA, N_ell_P_LA, Map_white_noise_levels = v3.AdvACT_noise(f_sky=fsky,ell_max=v3lmax+v3dell,delta_ell=\
v3dell)
elif v3mode == 5:
import szar.lat_noise_190528_w350ds4 as ccatp
tubes = (0, 0, 0, 2, 2, 1)
lat = ccatp.CcatLatv2(v3mode,
el=50.,
survey_years=4000 / 24. / 365.24,
survey_efficiency=1.0,
N_tubes=tubes)
vfreqs = lat.get_bands() # v3.Simons_Observatory_V3_LA_bands()
print("CCATP")
print("Replacing ", freqs, " with ", vfreqs)
N_bands = len(vfreqs)
freqs = vfreqs
vbeams = lat.get_beams() #v3.Simons_Observatory_V3_LA_beams()
print("Replacing ", fwhms, " with ", vbeams)
fwhms = vbeams
v3lmax = self.evalells.max()
v3dell = np.diff(self.evalells)[0]
print("Using ", fsky, " for fsky")
v3ell, N_ell_T_LA_full, N_ell_P_LA = lat.get_noise_curves(
fsky,
v3lmax + v3dell,
v3dell,
full_covar=True,
deconv_beam=True)
N_ell_T_LA = np.diagonal(N_ell_T_LA_full).T
Map_white_noise_levels = lat.get_white_noise(fsky)**.5
#print(freqs)
if (len(freqs) > 1):
fq_mat = np.matlib.repmat(freqs, len(freqs), 1)
fq_mat_t = np.transpose(np.matlib.repmat(freqs, len(freqs), 1))
else:
fq_mat = freqs
fq_mat_t = freqs
#print(fq_mat)
#initializing the weighting functions for the ilc
#thermal SZ weights
self.W_ll_tsz = np.zeros([len(self.evalells), len(np.array(freqs))])
#CMB weights
self.W_ll_cmb = np.zeros([len(self.evalells), len(np.array(freqs))])
#rayleigh scattering cross correlation weights
self.W_ll_rsx = np.zeros([len(self.evalells), len(np.array(freqs))])
#rayleigh scattering cross E-mode of CMB correlation weights
self.W_ll_rsxEE = np.zeros([len(self.evalells), len(np.array(freqs))])
#rayleigh scattering cross correlation weights NO foregrounds
self.W_ll_rsx_NF = np.zeros([len(self.evalells), len(np.array(freqs))])
#thermal SZ constraining the CIB weights
self.W_ll_tsz_c_cib = np.zeros(
[len(self.evalells), len(np.array(freqs))])
#thermal SZ constraining the CMB weights
self.W_ll_tsz_c_cmb = np.zeros(
[len(self.evalells), len(np.array(freqs))])
#CMB constraining the thermal SZ weights
self.W_ll_cmb_c_tsz = np.zeros(
[len(self.evalells), len(np.array(freqs))])
self.freq = freqs
#frequency functions for
f_nu_tsz = f_nu(self.cc.c, np.array(freqs)) #tSZ
f_nu_cmb = f_nu_tsz * 0.0 + 1. #CMB
f_nu_cib = self.fgs.f_nu_cib(np.array(freqs)) #CIB
f_nu_rsx = self.fgs.rs_nu(np.array(freqs)) #Rayleigh Cross
for ii in range(len(self.evalells)):
cmb_els = fq_mat * 0.0 + self.cc.clttfunc(self.evalells[ii])
cmb_ee = fq_mat * 0.0 + self.cc.cleefunc(self.evalells[ii])
if v3mode < 0:
inst_noise = (old_div(
noise_func(self.evalells[ii],
np.array(fwhms),
np.array(rms_noises),
lknee,
alpha,
dimensionless=False), self.cc.c['TCMBmuK']**2.))
nells = np.diag(inst_noise)
elif v3mode <= 2:
nells = N_ell_T_LA_full[:, :, ii] / self.cc.c['TCMBmuK']**2.
elif v3mode == 3:
ndiags = []
for ff in range(len(freqs)):
inst_noise = old_div(N_ell_T_LA[ff, ii],
self.cc.c['TCMBmuK']**2.)
ndiags.append(inst_noise)
nells = np.diag(np.array(ndiags))
elif v3mode == 5:
nells = N_ell_T_LA_full[:, :, ii] / self.cc.c['TCMBmuK']**2.
totfg = (self.fgs.rad_ps(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.cib_p(self.evalells[ii],fq_mat,fq_mat_t) +
self.fgs.cib_c(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.tSZ_CIB(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
totfgrs = (self.fgs.rad_ps(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.cib_p(self.evalells[ii],fq_mat,fq_mat_t) +
self.fgs.cib_c(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.rs_auto(self.evalells[ii],fq_mat,fq_mat_t) + \
self.fgs.tSZ_CIB(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii] ) * 2.* np.pi
fgrspol = (self.fgs.rad_pol_ps(self.evalells[ii],fq_mat,fq_mat_t)+self.fgs.gal_dust_pol(self.evalells[ii],fq_mat,fq_mat_t)+self.fgs.gal_sync_pol(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii] ) * 2.* np.pi
totfg_cib = (self.fgs.rad_ps(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.tSZ_CIB(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
ksz = fq_mat * 0.0 + self.fgs.ksz_temp(
self.evalells[ii]) / self.cc.c['TCMBmuK']**2. / (
(self.evalells[ii] + 1.) * self.evalells[ii]) * 2. * np.pi
tsz = self.fgs.tSZ(
self.evalells[ii], fq_mat,
fq_mat_t) / self.cc.c['TCMBmuK']**2. / (
(self.evalells[ii] + 1.) * self.evalells[ii]) * 2. * np.pi
cib = (self.fgs.cib_p(self.evalells[ii],fq_mat,fq_mat_t) + self.fgs.cib_c(self.evalells[ii],fq_mat,fq_mat_t)) \
/ self.cc.c['TCMBmuK']**2. / ((self.evalells[ii]+1.)*self.evalells[ii]) * 2.* np.pi
N_ll_for_tsz = nells + totfg + cmb_els + ksz
N_ll_for_cmb = nells + totfg + tsz + ksz
N_ll_for_rsx = nells + totfg + tsz + ksz + cmb_els
N_ll_for_rsxEE = nells + fgrspol + cmb_ee
N_ll_for_tsz_c_cmb = nells + totfg
N_ll_for_cmb_c_tsz = N_ll_for_tsz_c_cmb + ksz
N_ll_for_tsz_c_cib = nells + totfg_cib + cmb_els + ksz
N_ll_for_tsz_inv = np.linalg.inv(N_ll_for_tsz)
N_ll_for_cmb_inv = np.linalg.inv(N_ll_for_cmb)
N_ll_for_rsx_inv = np.linalg.inv(N_ll_for_rsx)
N_ll_for_tsz_c_cmb_inv = np.linalg.inv(N_ll_for_tsz_c_cmb)
N_ll_for_cmb_c_tsz_inv = N_ll_for_tsz_c_cmb_inv
N_ll_for_tsz_c_cib_inv = np.linalg.inv(N_ll_for_tsz_c_cib)
N_ll_noFG = nells
self.W_ll_tsz[ii, :] = weightcalculator(f_nu_tsz, N_ll_for_tsz)
self.W_ll_rsx[ii, :] = weightcalculator(f_nu_rsx, N_ll_for_rsx)
self.W_ll_rsx_NF[ii, :] = weightcalculator(f_nu_rsx, N_ll_noFG)
self.W_ll_rsxEE[ii, :] = weightcalculator(f_nu_rsx, N_ll_for_rsxEE)
self.W_ll_cmb[ii, :] = weightcalculator(f_nu_cmb, N_ll_for_cmb)
self.N_ll_tsz[ii] = np.dot(
np.transpose(self.W_ll_tsz[ii, :]),
np.dot(N_ll_for_tsz, self.W_ll_tsz[ii, :]))
self.N_ll_cmb[ii] = np.dot(
np.transpose(self.W_ll_cmb[ii, :]),
np.dot(N_ll_for_cmb, self.W_ll_cmb[ii, :]))
self.N_ll_rsx[ii] = np.dot(
np.transpose(self.W_ll_rsx[ii, :]),
np.dot(N_ll_for_rsx, self.W_ll_rsx[ii, :]))
self.N_ll_rsxEE[ii] = np.dot(
np.transpose(self.W_ll_rsxEE[ii, :]),
np.dot(N_ll_for_rsxEE, self.W_ll_rsxEE[ii, :]))
self.N_ll_rsx_NoFG[ii] = np.dot(
np.transpose(self.W_ll_rsx_NF[ii, :]),
np.dot(N_ll_noFG, self.W_ll_rsx_NF[ii, :]))
self.W_ll_tsz_c_cmb[ii, :] = constweightcalculator(
f_nu_cmb, f_nu_tsz, N_ll_for_tsz_c_cmb_inv)
self.W_ll_tsz_c_cib[ii, :] = constweightcalculator(
f_nu_cib, f_nu_tsz, N_ll_for_tsz_c_cib_inv)
self.W_ll_cmb_c_tsz[ii, :] = constweightcalculator(
f_nu_tsz, f_nu_cmb, N_ll_for_cmb_c_tsz_inv)
self.N_ll_tsz_c_cmb[ii] = np.dot(
np.transpose(self.W_ll_tsz_c_cmb[ii, :]),
np.dot(N_ll_for_tsz_c_cmb, self.W_ll_tsz_c_cmb[ii, :]))
self.N_ll_cmb_c_tsz[ii] = np.dot(
np.transpose(self.W_ll_cmb_c_tsz[ii, :]),
np.dot(N_ll_for_cmb_c_tsz, self.W_ll_cmb_c_tsz[ii, :]))
self.N_ll_tsz_c_cib[ii] = np.dot(
np.transpose(self.W_ll_tsz_c_cib[ii, :]),
np.dot(N_ll_for_tsz_c_cib, self.W_ll_tsz_c_cib[ii, :]))
'''
