alms_fg = np.array([Tlm_fg, Elm_fg, Blm_fg])
for f in frequencies:
outfilename = PLANCK_DATA_PATH + 'bispectrum_alms/' + '{}_planck_{}GHz_lmax{}'.format(
filebase, f, lmax_bispectrum)
I, Q, U = simulate_observed_map(alms_cmb=alms_cmb,
alms_fg=alms_fg,
frequency=f,
smear=smear,
experiment='planck',
nside=nside,
npix=None,
lmax=lmax)
print 'simulated maps for {}, {} GHz'.format(filebase, f)
Tlm, Elm, Blm = calc_alm(I,
Q,
U,
mask=mask,
lmax=lmax_bispectrum,
add_beam=None,
div_beam=None,
healpy_format=False)
print 'Calculated alms for {}, {} GHz'.format(filebase, f)
np.save(outfilename + '_Tlm.npy', Tlm)
np.save(outfilename + '_Elm.npy', Elm)
np.save(outfilename + '_Blm.npy', Blm)
def calibrate_fsky(mode,
nsim=1,
smear=True,
apo=2,
psky=70,
f=353,
nside=2048,
lmax=1000,
visual_check=False,
beam_file=pf.PLANCK_DATA_PATH +
'HFI_RIMO_Beams-100pc_R2.00.fits',
mask_sources=True,
put_mask=True):
"""No noise treatment here"""
fsky_correction = []
TTm = np.zeros(lmax + 1)
EEm = np.zeros(lmax + 1)
BBm = np.zeros(lmax + 1)
if put_mask:
print 'reading mask...'
mask = pf.get_planck_mask(psky=psky,
mask_sources=mask_sources,
apodization=apo)
else:
mask = None
print 'reading beams...'
hdulist = pf.fits.open(beam_file)
beam = hdulist[pf.BEAM_INDEX['{}'.format(f)]].data.NOMINAL[0][:lmax + 1]
beamP = hdulist[pf.BEAM_INDEX['{}P'.format(f)]].data.NOMINAL[0][:lmax + 1]
beam = beam[:lmax + 1]
beamP = beamP[:lmax + 1]
if mode == 'fg':
ls, cls_theory = get_theory_fg(f=f, lmax=lmax, psky=psky, apo=apo)
if mode == 'cmb':
ls, cls_theor = pf.get_theory_cmb(lmax=lmax, mode='cl')
factor = ls * (1 + ls)
for i in np.arange(nsim):
print 'sim #{}...'.format(i + 1)
I, Q, U = pf.simulate_cmb_map(nside=nside,
lmax=lmax,
frequency=f,
smear=smear,
cls_theory=cls_theory,
beam=beam,
beamP=beamP,
save=False,
beam_file=None)
print 'Cl #{}...'.format(i + 1)
Tlm, Elm, Blm = pf.calc_alm(I,
Q,
U,
mask=mask,
lmax=lmax,
add_beam=None,
add_beamP=None,
div_beam=beam,
div_beamP=beamP,
healpy_format=True)
TT = hp.alm2cl(Tlm)
EE = hp.alm2cl(Elm)
BB = hp.alm2cl(Blm)
#ls, TT, EE, BB, TE, TB, EB = measure_dlcl(mask=mask, Imap=I, Qmap=Q, Umap=U,
# beam=beam, beamP=beamP,
# mode='cl',frequency=f,
# lmax=lmax,lmin=0,
# put_mask=put_mask,
# psky=psky,
# mask_sources=mask_sources,
# apodization=apo)
TTm += TT / nsim
EEm += EE / nsim
BBm += BB / nsim
if visual_check:
hp.mollview(I)
hp.mollview(Q)
hp.mollview(U)
fsky_correction.append(cls_theory[0] / TTm)
fsky_correction.append(cls_theory[1] / EEm)
fsky_correction.append(cls_theory[2] / BBm)
fsky_correction = np.array(fsky_correction)
fsky_correction[np.isnan(fsky_correction)] = 0.
fsky_correction[fsky_correction == np.inf] = 0.
return ls, fsky_correction, TTm, EEm, BBm, cls_theory
import healpy as hp
frequencies = [353]#[100,143,353,217]
smear = True
mask_sources = True
nside=2048
psky = 70
apodization = 2
lmax_bispectrum = 199
mask = get_planck_mask(psky=psky,
mask_sources=mask_sources,
apodization=apodization,
smask_name='HFI_Mask_PointSrc_2048_R2.00.fits')
for f in frequencies:
outfilename = PLANCK_DATA_PATH + 'bispectrum_alms/' + 'data_planck_{}GHz_lmax{}'.format(f,lmax_bispectrum)
mapname = PLANCK_DATA_PATH + 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(f)
print 'reading map from file: {} ...'.format(mapname)
I,Q,U = hp.read_map(mapname, field=(0,1,2))
Tlm,Elm,Blm = calc_alm(I, Q, U, mask=mask,
lmax=lmax_bispectrum,add_beam=None,div_beam=None,
healpy_format=False)
np.save(outfilename + '_Tlm.npy', Tlm)
np.save(outfilename + '_Elm.npy', Elm)
np.save(outfilename + '_Blm.npy', Blm)
def calibrate_fsky(mode, nsim=1,
smear=True,apo=2,
psky=70, f=353,
nside=2048,lmax=1000,
visual_check=False,
beam_file=pf.PLANCK_DATA_PATH+'HFI_RIMO_Beams-100pc_R2.00.fits',
mask_sources=True, put_mask=True):
"""No noise treatment here"""
fsky_correction = []
TTm = np.zeros(lmax + 1)
EEm = np.zeros(lmax + 1)
BBm = np.zeros(lmax + 1)
if put_mask:
print 'reading mask...'
mask = pf.get_planck_mask(psky=psky,
mask_sources=mask_sources,
apodization=apo)
else:
mask = None
print 'reading beams...'
hdulist = pf.fits.open(beam_file)
beam = hdulist[pf.BEAM_INDEX['{}'.format(f)]].data.NOMINAL[0][:lmax+1]
beamP = hdulist[pf.BEAM_INDEX['{}P'.format(f)]].data.NOMINAL[0][:lmax+1]
beam = beam[:lmax+1]
beamP = beamP[:lmax+1]
if mode == 'fg':
ls, cls_theory = get_theory_fg(f=f, lmax=lmax, psky=psky, apo=apo)
if mode == 'cmb':
ls, cls_theor = pf.get_theory_cmb(lmax=lmax, mode='cl')
factor = ls*(1+ls)
for i in np.arange(nsim):
print 'sim #{}...'.format(i+1)
I, Q, U = pf.simulate_cmb_map(nside=nside, lmax=lmax,
frequency=f,smear=smear,
cls_theory=cls_theory,
beam=beam, beamP=beamP,
save=False,
beam_file=None)
print 'Cl #{}...'.format(i+1)
Tlm, Elm, Blm = pf.calc_alm(I, Q, U, mask=mask,
lmax=lmax,
add_beam=None,add_beamP=None,
div_beam=beam,div_beamP=beamP,
healpy_format=True)
TT = hp.alm2cl(Tlm)
EE = hp.alm2cl(Elm)
BB = hp.alm2cl(Blm)
#ls, TT, EE, BB, TE, TB, EB = measure_dlcl(mask=mask, Imap=I, Qmap=Q, Umap=U,
# beam=beam, beamP=beamP,
# mode='cl',frequency=f,
# lmax=lmax,lmin=0,
# put_mask=put_mask,
# psky=psky,
# mask_sources=mask_sources,
# apodization=apo)
TTm += TT/nsim; EEm += EE/nsim; BBm += BB/nsim
if visual_check:
hp.mollview(I)
hp.mollview(Q)
hp.mollview(U)
fsky_correction.append(cls_theory[0] / TTm)
fsky_correction.append(cls_theory[1] / EEm)
fsky_correction.append(cls_theory[2] / BBm)
fsky_correction = np.array(fsky_correction)
fsky_correction[np.isnan(fsky_correction)] = 0.
fsky_correction[fsky_correction==np.inf] = 0.
return ls, fsky_correction, TTm, EEm, BBm, cls_theory
