def generate_model(ra0, dec0, pointing_params, repeats=1, cross_scan=False,
span_angles=False, band="red", map_header=None, npixels_per_sample=0):
"""
Generate a PACS projector.
"""
pointing1 = tm.pacs_create_scan(ra0, dec0, **pointing_params)
# create obs object
obs1 = tm.PacsSimulation(pointing1, band)
if map_header is None:
map_header = obs1.get_map_header()
# create projector
projection1 = tm.Projection(obs1, header=map_header,
npixels_per_sample=npixels_per_sample)
P = lo.aslinearoperator(projection1)
# cross scan
if cross_scan:
pointing_params["scan_angle"] += 90.
pointing2 = tm.pacs_create_scan(ra0, dec0, **pointing_params)
obs2 = tm.PacsSimulation(pointing2, band)
projection2 = tm.Projection(obs2, header=map_header,
npixels_per_sample=npixels_per_sample)
P2 = lo.aslinearoperator(projection2)
P = lo.concatenate((P, P2))
# repeats
if repeats > 1:
P = lo.concatenate((P, ) * repeats)
if span_angles:
if cross_scan:
raise ValueError("Span_angles and cross_scan are incompatible.")
# equally spaced but exclude 0 and 90
angles = np.linspace(0, 90, repeats + 2)[1:-1]
for a in angles:
pointing_params["scan_angle"] = a
pointing2 = tm.pacs_create_scan(ra0, dec0, **pointing_params)
obs2 = tm.PacsSimulation(pointing2, band)
projection2 = tm.Projection(obs2, header=map_header,
npixels_per_sample=npixels_per_sample)
P2 = lo.aslinearoperator(projection2)
P = lo.concatenate((P, P2))
# noise
N = generate_noise_filter(obs1, P, band)
# covered area
map_shape = map_header['NAXIS2'], map_header['NAXIS1']
coverage = (P.T * np.ones(P.shape[0])).reshape(map_shape)
seen = coverage != 0
M = lo.decimate(coverage < 10)
# global model
H = N * P * M.T
return H
import tamasis as tm
import csh # my PACS inversion routines
import fitsarray as fa
import lo
# scan parameters
ra0, dec0 = 30., 30.
pointing_params = {
'cam_angle': 0.,
"scan_angle": 0.,
"scan_nlegs": 1,
"scan_length": 400.,
"scan_speed": 60.0,
"compression_factor": 1 # we will compress later
}
pointing = tm.pacs_create_scan(ra0, dec0, **pointing_params)
# create obs object
obs = tm.PacsSimulation(pointing, "red", policy_bad_detector="keep")
# we dont need 200 first and last frames
obs.pointing.removed[200:] = True
obs.pointing.removed[-201:] = True
# create projector
projection = tm.Projection(obs, npixels_per_sample=4)
P = lo.aslinearoperator(projection)
# simulate data
x0 = tm.gaussian(projection.shapein, 3) # map with gaussian source
tod0 = projection(x0) # data
n = np.random.randn(*tod0.shape) # noise
nsr = 1e-2
tod = tod0 + nsr * n # noisy data
y = tod.ravel() # as 1d array
# python setup.py install
# define projection matrix
# ------------------------
# center of the map
ra0, dec0 = 30., 30.
# let us define a small scan with 1 leg for testing purposes :
pointing_params = {
'cam_angle':0.,
"scan_angle":0.,
"scan_nlegs":1,
"scan_length":4.,
"compression_factor":1,
"scan_speed":60.0
}
pointing = tm.pacs_create_scan(ra0, dec0, **pointing_params)
# create obs object
obs = tm.PacsSimulation(pointing, "red", policy_bad_detector="keep")
# to define my compression matrix I need only 8 frames
# so I mask the others :
obs.pointing.removed[:] = True
obs.pointing.removed[201:209] = False
# create projector
projection = tm.Projection(obs)
P = lo.aslinearoperator(projection)
# define masking of coefficients
# ------------------------------
# number of coefficients to keep
factor = 8.
nk = P.shape[0] / factor
