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