def get_maps_cl(frec, fconv=None, lmin=20, delta_ell=40, apodization_degrees=5.):
mrec, resid, seenmap = get_maps_residuals(frec, fconv=fconv)
sh = np.shape(mrec)
print(sh, np.shape(resid))
nbsub = sh[1]
ns = hp.npix2nside(sh[2])
from qubic import apodize_mask
mymask = apodize_mask(seenmap, apodization_degrees)
# Create XPol object
from qubic import Xpol
lmax = 2 * ns
xpol = Xpol(mymask, lmin, lmax, delta_ell)
ell_binned = xpol.ell_binned
nbins = len(ell_binned)
# Pixel window function
pw = hp.pixwin(ns)
pwb = xpol.bin_spectra(pw[:lmax + 1])
# Calculate all crosses and auto
m_autos = np.zeros((nbsub, 6, nbins))
s_autos = np.zeros((nbsub, 6, nbins))
m_cross = np.zeros((nbsub, 6, nbins))
s_cross = np.zeros((nbsub, 6, nbins))
fact = ell_binned * (ell_binned + 1) / 2. / np.pi
for isub in range(nbsub):
m_autos[isub, :, :], s_autos[isub, :, :], m_cross[isub, :, :], s_cross[isub, :, :] = \
allcross_par(xpol, mrec[:, isub, :, :], silent=False, verbose=0)
return mrec, resid, seenmap, ell_binned, m_autos * fact / pwb ** 2, \
s_autos * fact / pwb ** 2, m_cross * fact / pwb ** 2, s_cross * fact / pwb ** 2
def get_xpol(seenmap, ns, lmin=20, delta_ell=20, apodization_degrees=5.):
"""
Returns a Xpoll object to get spectra, the bin used and the pixel window function.
"""
# Create a mask
mymask = apodize_mask(seenmap, apodization_degrees)
# Create XPol object
lmax = 2 * ns
xpol = Xpol(mymask, lmin, lmax, delta_ell)
ell_binned = xpol.ell_binned
# Pixel window function
pw = hp.pixwin(ns)
pwb = xpol.bin_spectra(pw[:lmax + 1])
return xpol, ell_binned, pwb
racenter = 0.0
deccenter = -57.0
maxang = 20.
center = equ2gal(racenter, deccenter)
veccenter = hp.ang2vec(pi/2-np.radians(center[1]), np.radians(center[0]))
vecpix = hp.pix2vec(nside, np.arange(12*nside**2))
cosang = np.dot(veccenter, vecpix)
maskok = np.degrees(np.arccos(cosang)) < maxang
maskmap = apodize_mask(maskok, 5)
hp.gnomview(maskmap, rot=[racenter, deccenter], coord=['G', 'C'], reso=15)
# Xpol estimation through MC
np.random.seed(0)
xpol = Xpol(maskmap, lmin, lmax, delta_ell)
ell_binned = xpol.ell_binned
spectra_binned = xpol.bin_spectra(spectra)
nbins = len(ell_binned)
nbmc = 100
allclsout = np.zeros((nbmc, 6, nbins))
allcls = np.zeros((nbmc, 6, lmax+1))
bar = progress_bar(nbmc)
for i in np.arange(nbmc):
maps = hp.synfast(spectra, nside, fwhm=0, pixwin=True, new=True,
verbose=False)
allcls[i], allclsout[i] = xpol.get_spectra(maps)
bar.update()
# plt.plot(nsubvals, np.sqrt(nsubvals), 'k', label='Optimal $\sqrt{N}$', lw=2)
# for i in xrange(3):
# plt.plot(nsubvals, mean_rms_cov[:,i] / mean_rms_cov[0,i] * np.sqrt(nsubvals), label=stokes[i], lw=2, ls='--')
# plt.xlabel('Number of sub-frequencies')
# plt.ylabel('Relative maps RMS')
# plt.legend()
#======================= Apply Xpoll to get spectra ============================
lmin = 20
lmax = 2 * ns
delta_ell = 20
#Xpoll needs a mask
mymask = apodize_mask(seenmap_conv, 5)
xpol = Xpol(mymask, lmin, lmax, delta_ell)
ell_binned = xpol.ell_binned
nbins = len(ell_binned)
print('nbins = {}'.format(nbins))
mcls, mcls_in = [], []
scls, scls_in = [], []
#Input : what we should find
mapsconv = np.zeros((12 * ns**2, 3))
#Output, what we find
maps_recon = np.zeros((12 * ns**2, 3))
for isub in xrange(len(nsubvals)):
sh = allmaps_conv[isub].shape
nside = 256
nsample = 1000
maxiter = 1000
path = '/home/fincardona/Qubic/map_making/maps/montecarlo'
cov = hp.read_map('%s/qcoverage_n{}_N{}_s{}_mi{}.fits'.format(
NFREQS, NPOINTS, nsample, maxiter) % path)
maskmap = cov > cov.max() * 0.2
lmin = 2
lmax = nside * 2
delta_ell = 20
xpol = Xpol(maskmap, lmin, lmax, delta_ell)
ell_binned = xpol.ell_binned
nbins = len(ell_binned)
reconstructed_maps = glob.glob('%s/fusion_n{}_N{}_s{}_mi{}*'.format(
NFREQS, NPOINTS, nsample, maxiter) % path)
all_spectra = np.empty((len(reconstructed_maps), 6, nbins))
for i, f in enumerate(reconstructed_maps):
rec_map = hp.read_map(f, field=(0,1,2))
biased, unbiased = xpol.get_spectra(rec_map)
all_spectra[i] = unbiased
Cls = all_spectra.mean(axis=0)
delta_Cls = all_spectra.std(axis=0)
# and now we need to multiply it by the beam. For gaussian beam:
delta_ell = 20
racenter = 0.0
deccenter = -57.0
maxang = 20.
center = equ2gal(racenter, deccenter)
veccenter = hp.ang2vec(pi / 2 - np.radians(center[1]), np.radians(center[0]))
vecpix = hp.pix2vec(nside, np.arange(12 * nside**2))
cosang = np.dot(veccenter, vecpix)
maskok = np.degrees(np.arccos(cosang)) < maxang
maskmap = apodize_mask(maskok, 5)
hp.gnomview(maskmap, rot=[racenter, deccenter], coord=['G', 'C'], reso=15)
# Xpol estimation through MC
np.random.seed(0)
xpol = Xpol(maskmap, lmin, lmax, delta_ell)
ell_binned = xpol.ell_binned
spectra_binned = xpol.bin_spectra(spectra)
nbins = len(ell_binned)
nbmc = 100
allclsout = np.zeros((nbmc, 6, nbins))
allcls = np.zeros((nbmc, 6, lmax + 1))
bar = progress_bar(nbmc)
for i in np.arange(nbmc):
maps = hp.synfast(spectra,
nside,
fwhm=0,
pixwin=True,
new=True,
verbose=False)
nside = 256
nsample = 1000
maxiter = 1000
path = '/home/fincardona/Qubic/map_making/maps/montecarlo'
cov = hp.read_map('%s/qcoverage_n{}_N{}_s{}_mi{}.fits'.format(
NFREQS, NPOINTS, nsample, maxiter) % path)
maskmap = cov > cov.max() * 0.2
lmin = 2
lmax = nside * 2
delta_ell = 20
xpol = Xpol(maskmap, lmin, lmax, delta_ell)
ell_binned = xpol.ell_binned
nbins = len(ell_binned)
reconstructed_maps = glob.glob(
'%s/fusion_n{}_N{}_s{}_mi{}*'.format(NFREQS, NPOINTS, nsample, maxiter) %
path)
all_spectra = np.empty((len(reconstructed_maps), 6, nbins))
for i, f in enumerate(reconstructed_maps):
rec_map = hp.read_map(f, field=(0, 1, 2))
biased, unbiased = xpol.get_spectra(rec_map)
all_spectra[i] = unbiased
Cls = all_spectra.mean(axis=0)
delta_Cls = all_spectra.std(axis=0)