clf()
for thera in all_ra: plot_onespot(np.array([thera, newspot[1]]), sadlc)
savefig('Chorillos_newspot.png')
####now with TOD
fech=1. # Hz
deltaaz=30
angspeed= 1. #deg/s
nsw = 100
#ptg = create_sweeping_pointings(spot_bicep2, 24., 1./fech, angspeed, deltaaz, nsw, 0, 0,
# date_obs = '2016-01-01 00:00:00', latitude=domec[0], longitude=domec[1])
ptg = create_sweeping_pointings(spot_bicep2, 24., 1./fech, angspeed, deltaaz, nsw, 0, 0,
date_obs = '2016-01-01 00:00:00', latitude=sadlc[0], longitude=sadlc[1])
ok = ptg.elevation > 30
fractime = ok.sum()/len(ok)*100
ptg = ptg[ptg.elevation > 30]
clf()
subplot(3,1,1)
plot(ptg.azimuth, ptg.elevation)
subplot(3,1,2)
plot(ptg.time, ptg.azimuth)
subplot(3,1,3)
plot(ptg.time, ptg.elevation)
clf()
subplot(2,2,1)
title('Equatorial')
# parameters
nside = 256
racenter = 0.0 # deg
deccenter = -57.0 # deg
angspeed = 1 # deg/sec
delta_az = 15. # deg
angspeed_psi = 0.1 # deg/sec
maxpsi = 45. # deg
nsweeps_el = 300
duration = 24 # hours
ts = 60 # seconds
# get the sampling model
np.random.seed(0)
sampling = create_sweeping_pointings([racenter, deccenter], duration, ts,
angspeed, delta_az, nsweeps_el,
angspeed_psi, maxpsi)
scene = QubicScene(nside)
# get the acquisition model
acquisition = QubicAcquisition(150,
sampling,
scene,
synthbeam_fraction=0.99,
detector_tau=0.01,
detector_nep=1.e-17,
detector_fknee=1.,
detector_fslope=1)
# simulate the timeline
tod, x0_convolved = acquisition.get_observation(x0,
fslope = 1
ncorr = 10
# observation model
np.random.seed(0)
racenter = 0.0
deccenter = -57.0
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,
angspeed = 1 # deg/sec
delta_az = 20. # deg
angspeed_psi = 0.1 # deg/sec
maxpsi = 45. # deg
nsweeps_el = 300
duration = 24. # hours
ts = duration*3600/2**23 # seconds Chosen in order to have a power of 2 in
center = equ2gal(racenter, deccenter)
####### Create some sampling
center = equ2gal(racenter, deccenter)
sadlc = np.array([-24.18947, -66.472016])
sampling = create_sweeping_pointings(
[racenter, deccenter], duration, ts*10, angspeed, delta_az, nsweeps_el,
angspeed_psi, maxpsi, latitude=sadlc[0], longitude=sadlc[1])
ok = np.abs(sampling.elevation-50)<20
samplingok = sampling[ok]
samplingok.time = 3600*(((samplingok.time/3600 +36) % 24)-12)
clf()
subplot(2,1,1)
plot(samplingok.time/3600, samplingok.azimuth,',')
xlabel('Time [Hours]')
ylabel('Azimuth [Deg.]')
subplot(2,1,2)
plot(samplingok.time/3600, samplingok.elevation,',')
xlabel('Time [Hours]')