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 pipeline_dli(filename, output_file, keywords, verbose=False):
import numpy as np
import linear_operators as lo
import tamasis as tm
# verbosity
tm.var.verbose = verbose
# define observation
obs = tm.PacsObservation(filename, **keywords["PacsObservation"])
# extra masking
tm.step_scanline_masking(obs, **keywords["scanline_masking"])
# get data
tod = obs.get_tod(**keywords["get_tod"])
# deglitching
tm.step_deglitching(obs, tod, **keywords["deglitching"])
# median filtering
tod = tm.filter_median(tod, **keywords["filter_median"])
# define projector
projection = obs.get_projection_operator(**keywords["Projection"])
# build instrument model
response = tm.ResponseTruncatedExponentialOperator(
obs.pack(obs.instrument.detector.time_constant) /
obs.instrument.SAMPLING_PERIOD)
compression = tm.CompressionAverageOperator(obs.slice.compression_factor)
masking = tm.MaskOperator(tod.mask)
model = masking * compression * response * projection
# set tod masked values to zero
tod = masking(tod)
# N^-1 operator
invntt = tm.InvNttOperator(obs)
# for the dli algorithm
M = map_mask(tod, model, **keywords["map_mask"])
# recast model as a new-style LinearOperator from linear_operators package
H = lo.aslinearoperator(model) * M.T
N = lo.aslinearoperator(invntt)
# vectorize data so it is accepted by LinearOperators
y = tod.ravel()
Ds = [tm.DiscreteDifferenceOperator(axis=i, shapein=projection.shapein) \
for i in (0, 1)]
Ds = [lo.aslinearoperator(D) for D in Ds]
Ds = [D * M.T for D in Ds]
D = lo.concatenate(Ds)
# handle tau which needs to be an ndarray
keywords["dli"]["tau"] *= np.ones(D.shape[0])
algo = lo.DoubleLoopAlgorithm(H,
y,
D,
noise_covariance=N,
fmin_args=keywords["fmin_args"],
lanczos=keywords["lanczos"],
**keywords["dli"])
# optimize
xe = algo()
# reshape
xe = (M.T * xe).reshape(projection.shapein)
# recast as tamasis map
xe = tm.Map(xe)
# save
xe.save(output_file)
def pipeline_dli(filename, output_file, keywords, verbose=False):
import numpy as np
import linear_operators as lo
import tamasis as tm
# verbosity
tm.var.verbose = verbose
# define observation
obs = tm.PacsObservation(filename, **keywords["PacsObservation"])
# extra masking
tm.step_scanline_masking(obs, **keywords["scanline_masking"])
# get data
tod = obs.get_tod(**keywords["get_tod"])
# deglitching
tm.step_deglitching(obs, tod, **keywords["deglitching"])
# median filtering
tod = tm.filter_median(tod, **keywords["filter_median"])
# define projector
projection = obs.get_projection_operator(**keywords["Projection"])
# build instrument model
response = tm.ResponseTruncatedExponentialOperator(obs.pack(
obs.instrument.detector.time_constant) /
obs.instrument.SAMPLING_PERIOD)
compression = tm.CompressionAverageOperator(obs.slice.compression_factor)
masking = tm.MaskOperator(tod.mask)
model = masking * compression * response * projection
# set tod masked values to zero
tod = masking(tod)
# N^-1 operator
invntt = tm.InvNttOperator(obs)
# for the dli algorithm
M = map_mask(tod, model, **keywords["map_mask"])
# recast model as a new-style LinearOperator from linear_operators package
H = lo.aslinearoperator(model) * M.T
N = lo.aslinearoperator(invntt)
# vectorize data so it is accepted by LinearOperators
y = tod.ravel()
Ds = [tm.DiscreteDifferenceOperator(axis=i, shapein=projection.shapein) \
for i in (0, 1)]
Ds = [lo.aslinearoperator(D) for D in Ds]
Ds = [D * M.T for D in Ds]
D = lo.concatenate(Ds)
# handle tau which needs to be an ndarray
keywords["dli"]["tau"] *= np.ones(D.shape[0])
algo = lo.DoubleLoopAlgorithm(H, y, D, noise_covariance=N,
fmin_args=keywords["fmin_args"],
lanczos=keywords["lanczos"],
**keywords["dli"])
# optimize
xe = algo()
# reshape
xe = (M.T * xe).reshape(projection.shapein)
# recast as tamasis map
xe = tm.Map(xe)
# save
xe.save(output_file)
#!/usr/bin/env python
import numpy as np
import scipy
import linear_operators as lo
# Load the infamous Lena image from scipy
im = scipy.lena()
im = im[::4, ::4]
# Generate a convolution model with a 7x7 uniform kernel
model = lo.convolve_fftw3(im.shape, np.ones((7, 7)))
# convolve the original image
data = model * im.ravel()
# add noise to the convolved data
data += 1e0 * np.random.randn(*data.shape)
# define smoothness prior
#prior = lo.concatenate([lo.diff(im.shape, axis=i) for i in xrange(im.ndim)])
prior = lo.concatenate([lo.diff(im.shape, axis=i) for i in xrange(im.ndim)]
+ [lo.wavelet2(im.shape, "haar"),])
# generate algorithm
algo = lo.DoubleLoopAlgorithm(model, data, prior)
# start the estimation algorithm
xe = algo()
# reshape the output as the algorithm only handles vectors
xe.resize(im.shape)
