def get_qubic_map(instrument, sampling, scene, input_maps, withplanck=True, covlim=0.1):
acq = QubicAcquisition(instrument, sampling, scene, photon_noise=True, effective_duration=1)
C = acq.get_convolution_peak_operator()
coverage = acq.get_coverage()
observed = coverage > covlim * np.max(coverage)
acq_restricted = acq[:, :, observed]
H = acq_restricted.get_operator()
x0_convolved = C(input_maps)
if not withplanck:
pack = PackOperator(observed, broadcast='rightward')
y_noiseless = H(pack(x0_convolved))
noise = acq.get_noise()
y = y_noiseless + noise
invntt = acq.get_invntt_operator()
A = H.T * invntt * H
b = (H.T * invntt)(y)
preconditioner = DiagonalOperator(1 / coverage[observed], broadcast='rightward')
solution_qubic = pcg(A, b, M=preconditioner, disp=True, tol=1e-3, maxiter=1000)
maps = pack.T(solution_qubic['x'])
maps[~observed] = 0
else:
acq_planck = PlanckAcquisition(150, acq.scene, true_sky=x0_convolved)#, fix_seed=True)
acq_fusion = QubicPlanckAcquisition(acq, acq_planck)
map_planck_obs=acq_planck.get_observation()
H = acq_fusion.get_operator()
invntt = acq_fusion.get_invntt_operator()
y = acq_fusion.get_observation()
A = H.T * invntt * H
b = H.T * invntt * y
solution_fusion = pcg(A, b, disp=True, maxiter=1000, tol=1e-3)
maps = solution_fusion['x']
maps[~observed] = 0
x0_convolved[~observed] = 0
return(maps, x0_convolved, observed)
def get_coverage_onedet(idetector=231, angspeed=1., delta_az=15., angspeed_psi=0., maxpsi=15., nsweeps_el=100, duration=24, ts=1., decrange=2., decspeed=2., recenter=False):
print('##### Getting Coverage for: ')
print('## idetector = '+str(idetector))
print('## duration = '+str(duration))
print('## ts = '+str(ts))
print('## recenter = '+str(recenter))
print('## angspeed = '+str(angspeed))
print('## delta_az = '+str(delta_az))
print('## nsweeps_el = '+str(nsweeps_el))
print('## decrange = '+str(decrange))
print('## decspeed = '+str(decspeed))
print('## angspeed_psi = '+str(angspeed_psi))
print('## maxpsi = '+str(maxpsi))
print('##########################')
nside = 256
racenter = 0.0
deccenter = -57.0
pointing = pointings_modbyJC.create_sweeping_pointings(
[racenter, deccenter], duration, ts, angspeed, delta_az, nsweeps_el,
angspeed_psi, maxpsi, decrange=decrange, decspeed=decspeed, recenter=recenter)
pointing.angle_hwp = np.random.random_integers(0, 7, pointing.size) * 22.5
ntimes = len(pointing)
# get instrument model with only one detector
instrument = QubicInstrument('monochromatic')
mask_packed = np.ones(len(instrument.detector.packed), bool)
mask_packed[idetector] = False
mask_unpacked = instrument.unpack(mask_packed)
instrument = QubicInstrument('monochromatic', removed=mask_unpacked)
obs = QubicAcquisition(instrument, pointing)
convolution = obs.get_convolution_peak_operator()
projection = obs.get_projection_peak_operator(kmax=0)
coverage = projection.pT1()
return coverage
def map2TOD(input_map, pointing,kmax=2):
ns=hp.npix2nside(input_map.size)
qubic = QubicInstrument('monochromatic,nopol',nside=ns)
#### configure observation
obs = QubicAcquisition(qubic, pointing)
C = obs.get_convolution_peak_operator()
P = obs.get_projection_peak_operator(kmax=kmax)
H = P * C
# Produce the Time-Ordered data
tod = H(input_map)
input_map_conv = C(input_map)
return(tod,input_map_conv,P)
def get_tod(instrument, sampling, scene, input_maps, withplanck=True, covlim=0.1, photon_noise=True):
acq = QubicAcquisition(instrument, sampling, scene, photon_noise=photon_noise)
C = acq.get_convolution_peak_operator()
coverage = acq.get_coverage()
observed = coverage > covlim * np.max(coverage)
acq_restricted = acq[:, :, observed]
H = acq_restricted.get_operator()
x0_convolved = C(input_maps)
pack = PackOperator(observed, broadcast='rightward')
y_noiseless = H(pack(x0_convolved))
noise = acq.get_noise()
y = y_noiseless + noise
return (y_noiseless, noise, y)
angspeed = 1 # deg/sec
delta_az = 15.
angspeed_psi = 0.1
maxpsi = 45.
nsweeps_el = 300
duration = 24 # hours
ts = 20 # seconds
sampling = create_sweeping_pointings(
[racenter, deccenter], duration, ts, angspeed, delta_az, nsweeps_el,
angspeed_psi, maxpsi)
# acquisition model
acq = QubicAcquisition(150, sampling, kind='I', synthbeam_fraction=0.99,
detector_sigma=sigma, detector_fknee=fknee,
detector_fslope=fslope, detector_ncorr=ncorr)
C = acq.get_convolution_peak_operator()
P = acq.get_projection_operator()
H = P * C
# produce the Time-Ordered data
y = H(x0)
# noise
psd = _gaussian_psd_1f(len(acq.sampling), sigma=sigma, fknee=fknee,
fslope=fslope, sampling_frequency=1/ts)
invntt = acq.get_invntt_operator()
noise = acq.get_noise()
# map-making
coverage = P.pT1()
mask = coverage > 10
center_gal = 0, 90 center = gal2equ(center_gal[0], center_gal[1]) # sampling model np.random.seed(0) sampling = create_random_pointings(center, 1000, 10) # scene model scene = QubicScene(hp.npix2nside(x0.size), kind='I') # instrument model instrument = QubicInstrument(filter_nu=150e9) # acquisition model acq = QubicAcquisition(instrument, sampling, scene) x0_convolved = acq.get_convolution_peak_operator()(x0) H = acq.get_operator() coverage = H.T(np.ones(H.shapeout)) mask = coverage > 0 # restrict the scene to the observed pixels acq_restricted = acq[..., mask] H_restricted = acq_restricted.get_operator() x0_restricted = x0[mask] y = H_restricted(x0_restricted) invntt = acq_restricted.get_invntt_operator() # solve for x A = H_restricted.T * invntt * H_restricted b = H_restricted.T(invntt(y)) solution = pcg(
center_gal = 0, 90 center = gal2equ(center_gal[0], center_gal[1]) # sampling model np.random.seed(0) sampling = create_random_pointings(center, 1000, 10) # scene model scene = QubicScene(hp.npix2nside(x0.size), kind='I') # instrument model instrument = QubicInstrument(filter_nu=150e9) # acquisition model acq = QubicAcquisition(instrument, sampling, scene) x0_convolved = acq.get_convolution_peak_operator()(x0) H = acq.get_operator() coverage = H.T(np.ones(H.shapeout)) mask = coverage > 0 # restrict the scene to the observed pixels acq_restricted = acq[..., mask] H_restricted = acq_restricted.get_operator() x0_restricted = x0[mask] y = H_restricted(x0_restricted) invntt = acq_restricted.get_invntt_operator() # solve for x A = H_restricted.T * invntt * H_restricted b = H_restricted.T(invntt(y)) solution = pcg(A,
ts = 20 # seconds
sampling = create_sweeping_pointings([racenter, deccenter], duration, ts,
angspeed, delta_az, nsweeps_el,
angspeed_psi, maxpsi)
scene = QubicScene(nside, kind='I')
# acquisition model
acq = QubicAcquisition(150,
sampling,
scene,
synthbeam_fraction=0.99,
detector_fknee=fknee,
detector_fslope=fslope,
detector_ncorr=ncorr)
H_ga = acq.get_operator()
C = acq.get_convolution_peak_operator()
H = H_ga * C
# produce the Time-Ordered data
y = H(x0)
# noise
sigma = acq.instrument.detector.nep / np.sqrt(2 * sampling.period)
psd = _gaussian_psd_1f(len(acq.sampling),
sigma=sigma,
fknee=fknee,
fslope=fslope,
sampling_frequency=1 / ts)
invntt = acq.get_invntt_operator()
noise = acq.get_noise()
noise[...] = 0
[racenter, deccenter], duration, ts, angspeed, delta_az, nsweeps_el,
angspeed_psi, maxpsi, decrange=decrange, decspeed=decspeed,recenter=True)
pointing.angle_hwp = np.random.random_integers(0, 7, pointing.size) * 22.5
ntimes = len(pointing)
# get instrument model with only one detector
idetector = 231
instrument = QubicInstrument('monochromatic')
instrument.plot()
mask_packed = np.ones(len(instrument.detector.packed), bool)
mask_packed[idetector] = False
mask_unpacked = instrument.unpack(mask_packed)
instrument = QubicInstrument('monochromatic', removed=mask_unpacked)
obs = QubicAcquisition(instrument, pointing)
convolution = obs.get_convolution_peak_operator()
projection = obs.get_projection_peak_operator(kmax=0)
hwp = obs.get_hwp_operator()
polarizer = obs.get_polarizer_operator()
coverage = projection.pT1()
mask = coverage > 0
projection.restrict(mask)
pack = PackOperator(mask, broadcast='rightward')
ra,dec=pointings_modbyJC._hor2equ(pointing.azimuth, pointing.elevation, pointing.latitude, pointing.time/3600)
ns_scan = int(delta_az / angspeed / 0.1)
ns_tot = ns_scan * 2
ichunk = (np.arange(len(pointing)) / ns_tot / nsweeps_el).astype(numpy.int64)
isweep = ((np.arange(len(pointing)) / ns_tot).astype(numpy.int64)) % nsweeps_el
clf()
subplot(2,2,1)
plot(pointing.azimuth,pointing.elevation)
