allclspoiled_noisy = np.zeros((6, nbins, len(strrnd)))
allclspoiled_res = np.zeros((6, nbins, len(strrnd)))
for kk in np.arange(len(strrnd)):
print(' - Doing '+strrnd[kk])
### Read the maps
covmap = qubic.io.read_map(rep+'cov_'+basestr+strrnd[kk]+'.fits')
spoiled_covmap = qubic.io.read_map(rep+'cov_'+basestr+'spoiled_'+strrnd[kk]+'.fits')
initmap = qubic.io.read_map(rep+'maps_'+basestr+noI+'input_'+strrnd[kk]+'.fits')
noiselessmap = qubic.io.read_map(rep+'maps_'+basestr+noI+'noiseless_'+strrnd[kk]+'.fits')
noisymap = qubic.io.read_map(rep+'maps_'+basestr+noI+'noisy_'+strrnd[kk]+'.fits')
spoiled_noiselessmap = qubic.io.read_map(rep+'maps_'+basestr+noI+'spoiled_noiseless_'+strrnd[kk]+'.fits')
spoiled_noisymap = qubic.io.read_map(rep+'maps_'+basestr+noI+'spoiled_noisy_'+strrnd[kk]+'.fits')
### Get the apodization mask if needed
if maskmap is None:
maskok = covmap != 0
maskmap = XPol.apodize_mask(maskok,apodize_fwhm,mapang=mapang)
### Get the Cls
allclinit[:,:,kk], newl, Mllmat, MllBinned, MllBinnedInv, p, q, allcls = XPol.get_spectra(initmap.T, maskmap, 2*ns, 25, 25, wl=wl, Mllmat=Mllmat, MllBinned=MllBinned, ellpq=ellpq, MllBinnedInv=MllBinnedInv)
allclnoiseless[:,:,kk], newl, Mllmat, MllBinned, MllBinnedInv, p, q, allcls = XPol.get_spectra(noiselessmap.T, maskmap, 2*ns, 25, 25, wl=wl, Mllmat=Mllmat, MllBinned=MllBinned, ellpq=ellpq, MllBinnedInv=MllBinnedInv)
allclnoisy[:,:,kk], newl, Mllmat, MllBinned, MllBinnedInv, p, q, allcls = XPol.get_spectra(noisymap.T, maskmap, 2*ns, 25, 25, wl=wl, Mllmat=Mllmat, MllBinned=MllBinned, ellpq=ellpq, MllBinnedInv=MllBinnedInv)
allclres[:,:,kk], newl, Mllmat, MllBinned, MllBinnedInv, p, q, allcls = XPol.get_spectra(noisymap.T-noiselessmap.T, maskmap, 2*ns, 25, 25, wl=wl, Mllmat=Mllmat, MllBinned=MllBinned, ellpq=ellpq, MllBinnedInv=MllBinnedInv)
allclspoiled_noiseless[:,:,kk], newl, Mllmat, MllBinned, MllBinnedInv, p, q, allcls = XPol.get_spectra(spoiled_noiselessmap.T, maskmap, 2*ns, 25, 25, wl=wl, Mllmat=Mllmat, MllBinned=MllBinned, ellpq=ellpq, MllBinnedInv=MllBinnedInv)
allclspoiled_noisy[:,:,kk], newl, Mllmat, MllBinned, MllBinnedInv, p, q, allcls = XPol.get_spectra(spoiled_noisymap.T, maskmap, 2*ns, 25, 25, wl=wl, Mllmat=Mllmat, MllBinned=MllBinned, ellpq=ellpq, MllBinnedInv=MllBinnedInv)
allclspoiled_res[:,:,kk], newl, Mllmat, MllBinned, MllBinnedInv, p, q, allcls = XPol.get_spectra(spoiled_noisymap.T - spoiled_noiselessmap.T, maskmap, 2*ns, 25, 25, wl=wl, Mllmat=Mllmat, MllBinned=MllBinned, ellpq=ellpq, MllBinnedInv=MllBinnedInv)
########### Make averages and rms
clinit[j,:,:] = np.mean(allclinit, axis = 2)
dclinit[j,:,:] = np.std(allclinit, axis = 2)
clnoiseless[j,:,:] = np.mean(allclnoiseless, axis = 2)
dclnoiseless[j,:,:] = np.std(allclnoiseless, axis = 2)
clnoisy[j,:,:] = np.mean(allclnoisy, axis = 2)
dclnoisy[j,:,:] = np.std(allclnoisy, axis = 2)
#### Mask racenter = 0.0 deccenter = -57.0 maxang = 20.0 center = equ2gal(racenter, deccenter) nsmaskinit = nside veccenter = hp.ang2vec(pi / 2 - np.radians(center[1]), np.radians(center[0])) vecpix = hp.pix2vec(nsmaskinit, np.arange(12 * nsmaskinit ** 2)) cosang = np.dot(veccenter, vecpix) maskok = np.degrees(np.arccos(cosang)) < maxang ### Make Mask Map mapang = XPol.map_ang_from_edges(maskok) maskmap = XPol.apodize_mask(maskok, 2, mapang=mapang) # hp.gnomview(maskmap,rot=[racenter,deccenter],coord=['G','C'],reso=15) wl = hp.anafast(maskmap, regression=False) wl = wl[0 : lmax + 1] maps = hp.synfast(spectra[1:], nside, fwhm=0, pixwin=True, new=True) cls, newl, Mll, MllBinned, MllBinnedInv, p, q, pseudocls = XPol.get_spectra(maps, maskmap, 2 * nside - 1, 20, 20) nbins = len(newl) nbmc = 100 allclsout = np.zeros((nbmc, 6, nbins)) allcls = np.zeros((nbmc, 6, lmax + 1))
