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 = tm.Projection(obs, **keywords["Projection"])
# build instrument model
response = tm.ResponseTruncatedExponential(obs.pack(
obs.instrument.detector.time_constant) / obs.SAMPLING_PERIOD)
compression = tm.CompressionAverage(obs.slice.compression_factor)
masking = tm.Masking(tod.mask)
model = masking * compression * response * projection
# set tod masked values to zero
tod = masking(tod)
# N^-1 operator
invntt = tm.InvNtt(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.DiscreteDifference(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 noise_covariance(tod, obs):
"""
Defines the noise covariance matrix
"""
length = 2 ** np.ceil(np.log2(np.array(tod.nsamples) + 200))
invNtt = tm.InvNtt(length, obs.get_filter_uncorrelated())
fft = tm.Fft(length)
padding = tm.Padding(left=invNtt.ncorrelations,
right=length - tod.nsamples - invNtt.ncorrelations)
masking = tm.Masking(tod.mask)
cov = masking * padding.T * fft.T * invNtt * fft * padding * masking
return cov
def generate_noise_filter(obs1, P, band):
# data
if band == "red":
shape0 = 512.
if band == "green" or band == "blue":
shape0 = 2048.
nsamples = P.shape[0] / shape0
# 1/f noise model
length = 2 ** np.ceil(np.log2(np.array(nsamples)))
# square root in Fourier !!!
filt = obs1.get_filter_uncorrelated()
fft0 = lo.fft2(filt.shape, axes=(1,))
filt2 = np.real(fft0.T(np.sqrt(fft0(filt))))
invNtt = tm.InvNtt(length, filt2)
fft = tm.FftHalfComplex(length)
padding = tm.Padding(left=invNtt.ncorrelations,
right=length - nsamples - invNtt.ncorrelations)
cov = padding.T * fft.T * invNtt * fft * padding
N12 = lo.aslinearoperator(cov)
return N12
def pipeline_wrls(filenames, output_file, keywords, verbose=False):
"""
Perform regularized least-square inversion of state of the art
PACS model. The PACS model includes tod mask, compression,
response, projection and noise covariance (invntt).
Processing steps are as follows :
- define PacsObservation instance
- mask first scanlines to avoid drift
- get Time Ordered Data (get_tod)
- 2nd level deglitching (with standard projector and tod median
filtering with a narrow window)
- median filtering
- define projection
- perform inversion on model
- save file
Arguments
---------
filenames: list of strings
List of data filenames.
output_file : string
Name of the output fits file.
keywords: dict
Dictionary containing options for all steps as dictionary.
verbose: boolean (default False)
Set verbosity.
Returns
-------
Returns nothing. Save result as a fits file.
"""
from scipy.sparse.linalg import cgs
import tamasis as tm
# verbosity
keywords["mapper_rls"]["verbose"] = verbose
# define observation
obs = tm.PacsObservation(filenames, **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 = tm.Projection(obs, **keywords["Projection"])
# build instrument model
response = tm.ResponseTruncatedExponential(
obs.pack(obs.instrument.detector.time_constant) / obs.SAMPLING_PERIOD)
compression = tm.CompressionAverage(obs.slice.compression_factor)
masking = tm.Masking(tod.mask)
model = masking * compression * response * projection
# set tod masked values to zero
tod = masking(tod)
# N^-1 operator
invntt = tm.InvNtt(obs)
# perform map-making inversion
map_rls = tm.mapper_rls(tod,
model,
invntt=invntt,
solver=cgs,
**keywords["mapper_rls"])
# save
map_rls.save(output_file)