def func(twosided):
np.random.seed(0)
p = _gaussian_psd_1f(nsamples,
fsamp,
white,
fknee,
alpha,
twosided=twosided)
t = _gaussian_sample(nsamples, fsamp, p, twosided=twosided)
return p, t
[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
P = P.restrict(mask, inplace=True)
unpack = UnpackOperator(mask)
# map without covariance matrix
solution1 = pcg(P.T * P, P.T(y + noise),
M=DiagonalOperator(1/coverage[mask]), disp=True)
x1 = unpack(solution1['x'])
# map with covariance matrix
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
# map-making
coverage = acq.get_coverage()
mask = coverage / coverage.max() > 0.01
acq_red = acq[..., mask]
H_ga_red = acq_red.get_operator()
# map without covariance matrix
solution1 = pcg(H_ga_red.T * H_ga_red,
H_ga_red.T(y + noise),
def func(twosided):
np.random.seed(0)
p = _gaussian_psd_1f(nsamples, fsamp, white, fknee, alpha,
twosided=twosided)
t = _gaussian_sample(nsamples, fsamp, p, twosided=twosided)
return p, t
