def test_noiseless():
sampling = create_random_pointings([0, 90], 100, 10)
acq_qubic = QubicAcquisition(150, sampling)
acq_planck = PlanckAcquisition(150, acq_qubic.scene, true_sky=SKY)
acq_fusion = QubicPlanckAcquisition(acq_qubic, acq_planck)
np.random.seed(0)
y1 = acq_fusion.get_observation()
np.random.seed(0)
y2 = acq_fusion.get_observation(noiseless=True) + acq_fusion.get_noise()
assert_same(y1, y2)
_max = [300, 5, 5]
for i, (inp, rec, iqu) in enumerate(zip(maps_convolved.T, maps_recon.T, 'IQU')):
inp[cov < cov.max() * 0.01] = hp.UNSEEN
rec[cov < cov.max() * 0.01] = hp.UNSEEN
diff = inp - rec
diff[cov < cov.max() * 0.01] = hp.UNSEEN
hp.gnomview(inp, rot=center_gal, reso=5, xsize=700, fig=1,
sub=(3, 3, i + 1), min=-_max[i], max=_max[i], title='Input, {}'.format(iqu))
hp.gnomview(rec, rot=center_gal, reso=5, xsize=700, fig=1,
sub=(3, 3, i + 4), min=-_max[i], max=_max[i], title='Recon, {}'.format(iqu))
hp.gnomview(diff, rot=center_gal, reso=5, xsize=700, fig=1,
sub=(3, 3, i+7), min=-_max[i], max=_max[i], title='Diff, {}'.format(iqu))
# Now let's play with the fusion acquisition
planck = PlanckAcquisition(band, a.scene, true_sky=maps_convolved)
acq_fusion = QubicPlanckAcquisition(a, acq_planck)
TOD, maps_convolved = a.get_observation(x0)
maps_recon_fusion = a.tod2map(TOD, tol=tol)
mp.figure(2)
for i, (inp, rec, iqu) in enumerate(zip(maps_convolved.T, maps_recon_fusion.T, 'IQU')):
inp[cov < cov.max() * 0.01] = hp.UNSEEN
rec[cov < cov.max() * 0.01] = hp.UNSEEN
diff = inp - rec
diff[cov < cov.max() * 0.01] = hp.UNSEEN
hp.gnomview(inp, rot=center_gal, reso=5, xsize=700, fig=1,
sub=(3, 3, i + 1), min=-_max[i], max=_max[i], title='Input, {}'.format(iqu))
hp.gnomview(rec, rot=center_gal, reso=5, xsize=700, fig=1,
sub=(3, 3, i + 4), min=-_max[i], max=_max[i], title='Recon, {}'.format(iqu))
hp.gnomview(diff, rot=center_gal, reso=5, xsize=700, fig=1,
def reconstruction(racenter, deccenter, NPOINTS, NFREQS, filter_name, scene,
maxiter, tol, rank, n, start):
sampling = create_random_pointings([racenter, deccenter], 1000, 10)
detector_nep = 4.7e-17 * np.sqrt(
len(sampling) * sampling.period / (365 * 86400))
#x0 = read_map(PATH + 'syn256_pol.fits')
x0 = np.zeros((12 * nside**2, 3))
q = MultiQubicInstrument(NPOINTS=NPOINTS,
NFREQS=NFREQS,
filter_name=filter_name,
detector_nep=detector_nep)
C_nf = q.get_convolution_peak_operator()
conv_sky_ = C_nf(x0)
fwhm_t = np.sqrt(q.synthbeam.peak150.fwhm**2 - C_nf.fwhm**2)
C_transf = HealpixConvolutionGaussianOperator(fwhm=fwhm_t)
acq = MultiQubicAcquisition(q, sampling, scene=scene)
H = acq.get_operator()
coverage = acq.get_coverage(H)
acq_planck = MultiPlanckAcquisition(np.int(filter_name / 1e9),
scene,
true_sky=conv_sky_)
acq_fusion = QubicPlanckAcquisition(acq, acq_planck)
H = acq_fusion.get_operator()
y = acq_fusion.get_observation()
invntt = acq_fusion.get_invntt_operator()
A = H.T * invntt * H
b = H.T * invntt * y
solution_fusion = pcg(A, b, disp=True, maxiter=maxiter, tol=tol)
x_rec_fusion_ = solution_fusion['x']
x_rec_fusion = C_transf(x_rec_fusion_)
#H = acq.get_operator()
#COV = acq.get_coverage(H)
#y = acq.get_observation(conv_sky_)
#invntt = acq.get_invntt_operator()
# A = H.T * invntt * H
# b = H.T * invntt * y
# solution_qubic = pcg(A, b, disp=True, maxiter=maxiter, tol=tol)
# x_rec_qubic_ = solution_qubic['x']
# x_rec_qubic = C_transf(x_rec_qubic_)
# conv_sky = C_transf(conv_sky_)
path = '/home/fincardona/Qubic/map_making/maps/montecarlo'
if rank == 0:
hp.write_map(
'%s/fusion_n{}_N{}_s{}_mi{}_niter{}.fits'.format(
NFREQS, NPOINTS, len(sampling), maxiter, n + start) % path,
x_rec_fusion.T)
#hp.write_map('maps_test/qubic_n{}_N{}_s{}_mi{}.fits'.format(
# NFREQS, NPOINTS, len(sampling), maxiter), x_rec_qubic.T)
gc.collect()
return coverage, sampling, path
