def write_reference():
np.random.seed(0)
p = create_random_pointings([0, 90], NPTG, ANGLE)
FitsArray([p.azimuth, p.elevation, p.pitch, p.angle_hwp]).save(FILEPTG)
for kind, map, filename in zip(['I', 'IQU'], [MAPIQU[..., 0], MAPIQU],
[FILETODI, FILETODIQU]):
acq = QubicAcquisition(INSTRUMENT, p, nside=256, kind=kind)
tod = acq.get_observation(map, noiseless=True)
FitsArray(tod).save(filename)
acq_planck = PlanckAcquisition(150, acq_qubic.scene, true_sky=convolved_sky)
acq_fusion = QubicPlanckAcquisition(acq_qubic, acq_planck)
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=maxiter, tol=tol)
acq_qubic = QubicAcquisition(150, sampling, scene, effective_duration=1)
H = acq_qubic.get_operator()
invntt = acq_qubic.get_invntt_operator()
y, sky_convolved = acq_qubic.get_observation(sky, convolution=True)
A = H.T * invntt * H
b = H.T * invntt * y
solution_qubic = pcg(A, b, disp=True, maxiter=maxiter, tol=tol)
# some display
def display(input, msg, iplot=1):
out = []
for i, (kind, lim) in enumerate(zip('IQU', [50, 5, 5])):
map = input[..., i]
out += [hp.gnomview(map, rot=center, reso=5, xsize=800, min=-lim,
max=lim, title=msg + ' ' + kind,
sub=(3, 3, iplot + i), return_projected_map=True)]
# read the input map
x0 = qubic.io.read_map(qubic.data.PATH + 'syn256_pol.fits', field='I_STOKES')
nside = 256
# let's take the galactic north pole as the center of the observation field
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 = QubicScene(nside, kind='I')
# acquisition model
acq = QubicAcquisition(150, sampling, scene)
y, x0_convolved = acq.get_observation(x0, convolution=True, noiseless=True)
# map-making
x, coverage = tod2map_all(acq, y, disp=True, tol=1e-4, coverage_threshold=0)
mask = coverage > 0
# some display
def display(x, title):
x = x.copy()
x[~mask] = np.nan
hp.gnomview(x, rot=center_gal, reso=5, xsize=600, min=-200, max=200,
title=title)
display(x0, 'Original map')
display(x0_convolved, 'Convolved original map')
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,
convolution=True,
noiseless=True)
# reconstruct using two methods
map_all, cov_all = tod2map_all(acquisition, tod, tol=1e-2)
map_each, cov_each = tod2map_each(acquisition, tod, tol=1e-2)
# some display
def display(map, cov, msg, sub):
for i, (kind, lim) in enumerate(zip('IQU', [200, 10, 10])):
map_ = map[..., i].copy()
mask = cov == 0
map_[mask] = np.nan
hp.gnomview(map_,
rot=center,
from __future__ import division
from qubic import (create_random_pointings, gal2equ, QubicAcquisition,
QubicScene, tod2map_all)
import numpy as np
import qubic
# read the input map
input_map = qubic.io.read_map(qubic.data.PATH + 'syn256_pol.fits',
field='I_STOKES')
nside = 256
# let's take the galactic north pole as the center of the observation field
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 = QubicScene(nside, kind='I')
# acquisition model
acq = QubicAcquisition(150, sampling, scene)
hit = acq.get_hitmap()
# Produce the Time-Ordered data
tod = acq.get_observation(input_map)
output_map, coverage = tod2map_all(acq, tod)
print(acq.comm.rank, output_map[coverage > 0][:5])
print(acq.comm.rank, hit[hit > 0][:10])
def func(kind, map, filename):
acq = QubicAcquisition(INSTRUMENT, p, nside=256, kind=kind)
tod = acq.get_observation(map, noiseless=True)
ref = FitsArray(filename)
np.testing.assert_almost_equal(tod, ref)
mask=mask)
acq_fusion = QubicPlanckAcquisition(acq_qubic, acq_planck)
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=maxiter, tol=tol)
acq_qubic = QubicAcquisition(150, sampling, scene, effective_duration=1)
H = acq_qubic.get_operator()
invntt = acq_qubic.get_invntt_operator()
y, sky_convolved = acq_qubic.get_observation(sky, convolution=True)
A = H.T * invntt * H
b = H.T * invntt * y
solution_qubic = pcg(A, b, disp=True, maxiter=maxiter, tol=tol)
# some display
def display(input, msg, iplot=1):
out = []
for i, (kind, lim) in enumerate(zip('IQU', [50, 5, 5])):
map = input[..., i]
out += [
hp.gnomview(map,
rot=center,
from qubic import (
create_random_pointings, gal2equ, QubicAcquisition, QubicScene,
tod2map_all)
import numpy as np
import qubic
# read the input map
input_map = qubic.io.read_map(qubic.data.PATH + 'syn256_pol.fits',
field='I_STOKES')
nside = 256
# let's take the galactic north pole as the center of the observation field
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 = QubicScene(nside, kind='I')
# acquisition model
acq = QubicAcquisition(150, sampling, scene)
hit = acq.get_hitmap()
# Produce the Time-Ordered data
tod = acq.get_observation(input_map)
output_map, coverage = tod2map_all(acq, tod)
print acq.comm.rank, output_map[coverage > 0][:5]
print acq.comm.rank, hit[hit > 0][:10]
