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
def cs(tod, factor, dtype=np.float64):
""" Compressed sensing compression mode"""
# shape
shape = tod.shape
# transform
H = lo.fht(shape, axes=0, dtype=dtype)
# mask
start_ind = np.resize(np.arange(factor), shape[0])
mask = np.ones(shape, dtype=bool)
for i in xrange(mask.shape[0]):
mask[i, start_ind[i]::factor] = 0.
# if frames number not a multiple of factor, mask
mask[:, factor * np.floor(shape[1] / float(factor)):] = 0.
M = lo.decimate(mask, dtype=dtype)
C = M * H
return C
def cs(tod, factor, dtype=np.float64):
""" Compressed sensing compression mode"""
# shape
shape = tod.shape
# transform
H = lo.fht(shape, axes=0, dtype=dtype)
# mask
start_ind = np.resize(np.arange(factor), shape[0])
mask = np.ones(shape, dtype=bool)
for i in xrange(mask.shape[0]):
mask[i, start_ind[i]::factor] = 0.
# if frames number not a multiple of factor, mask
mask[:, factor * np.floor(shape[1] / float(factor)):] = 0.
M = lo.decimate(mask, dtype=dtype)
C = M * H
return C
def map_mask(tod, model, threshold=10):
import linear_operators as lo
import numpy as np
return lo.decimate(model.T(np.ones(tod.shape)) < threshold)
def map_mask(tod, model, threshold=10):
import linear_operators as lo
import numpy as np
return lo.decimate(model.T(np.ones(tod.shape)) < threshold)
def decimate_temporal(tod, factor):
shape = tod.shape
mask = np.ones(shape, dtype=bool)
mask[:, 0::factor] = 0.
return lo.decimate(mask)
def decimate_temporal(tod, factor):
shape = tod.shape
mask = np.ones(shape, dtype=bool)
mask[:, 0::factor] = 0.
return lo.decimate(mask)
