def __call__(self, data, state):
# get parameters
C = state.get('compression')
factor = state.get('compression_factor', 1)
# decompress
uc_data = uncompress(data, C, factor)
uc_data.mask = state['mask']
# filter
uc_data = tm.filter_median(uc_data, length=self.filter_length)
tm.deglitch_l2mad(uc_data, state['model'])
# define mask
masking = tm.Masking(uc_data.mask)
# update model
state['deglitching_mask'] = uc_data.mask
state['deglitching_mask_lo'] = masking
state['model'] = masking * state['model']
state['deglitching_filter_length'] = self.filter_length
return data
npixels_per_sample=5) multiplexing = CompressionAverageOperator(1) crosstalk = IdentityOperator() compression = CompressionAverageOperator(4) model = compression * crosstalk * multiplexing * projection * telescope # read the Tod off the disk tod40Hz = pacs.get_tod(flatfielding=True, subtraction_mean=True) # remove drift tod40Hz_filtered = filter_polynomial(tod40Hz, 6) drift = tod40Hz - tod40Hz_filtered tod40Hz = filter_median(tod40Hz_filtered, 10000) # second level deglitching tod40Hz.mask = deglitch_l2mad(tod40Hz, projection, nsigma=5) idetector=5 plot_tod((tod40Hz+drift)[idetector]) plot(drift[idetector], 'r') masking = MaskOperator(tod40Hz.mask) model40Hz = masking * projection map_naive40Hz = mapper_naive(tod40Hz, model40Hz) map_naive40Hz.imshow() # clim doesn't work anymore with AnnotatedImage #clim(-0.00002,0.00002)
tod = obs.get_tod()
header = obs.get_map_header()
header.update('CDELT1', resolution / 3600)
header.update('CDELT2', resolution / 3600)
npix = 5
good_npix = False
projection = tm.Projection(obs, header=header,
resolution=resolution,
oversampling=False,
npixels_per_sample=npix)
model = projection
#C = csh.averaging(tod.shape, factor=factor)
compression_shape = [tod.size / factor, tod.size, ]
C = tm.CompressionAverage(compression_factor=factor).aslinearoperator(compression_shape)
C = lo.aslinearoperator(C)
# compress data
ctod = compress(tod, C, factor)
# uncompress for preprocessing
uctod = uncompress(ctod, C, factor)
# XXX need mask ...
uctod.mask = tod.mask
# deglitching
uctod = tm.filter_median(uctod, length=10)
uctod.mask = tm.deglitch_l2mad(uctod, projection)
masking = tm.Masking(uctod.mask)
model = masking * projection
# filtering
ctod = tm.filter_median(ctod, length=filter_length / factor)
cov = noise_covariance(ctod, obs)
S = cov.aslinearoperator()
pacs = tm.PacsObservation(filename=filenames,
fine_sampling_factor=1, keep_bad_detectors=False)
# reset pacs header to have a shape multiple of 4
#header = pacs.get_map_header()
#header['NAXIS1'] = 192
#header['NAXIS2'] = 192
#header['CRPIX1'] = 96
#header['CRPIX2'] = 96
# data
tod = pacs.get_tod()
# remove bad pixels (by updating mask !)
#tod = remove_bad_pixels(tod)
# deglitching
#projection = tm.Projection(pacs, header=header, resolution=3., npixels_per_sample=6)
projection = tm.Projection(pacs, resolution=3., npixels_per_sample=6)
tm.deglitch_l2mad(tod, projection)
# model
masking = tm.Masking(tod.mask)
model = masking * projection
# remove drift
tod = tm.filter_median(tod, length=999)
model = masking * projection
# naive map
backmap = model.transpose(tod)
# coverage map
weights = model.transpose(tod.ones(tod.shape))
# mask on map
mask = weights == 0
M = lo.mask(mask)
# preconditionner
# compress data
factor = 8
compression = tm.CompressionAverage(factor)
C = lo.aslinearoperator(
compression.aslinearoperator(shape=(tod.size / factor, tod.size)))
ctod = compression.direct(tod)
# uncompress for deglitching
uctod = tod.copy(tod.shape)
y0, t = lo.spl.cgs(C.T * C, C.T * ctod.flatten())
uctod[:] = y0.reshape(tod.shape)
# deglitching
projection = tm.Projection(pacs,
header=header,
resolution=3.,
npixels_per_sample=5)
uctod.mask = tm.deglitch_l2mad(uctod, projection)
ctod = compression.direct(uctod)
# model
masking = tm.Masking(uctod.mask)
model = compression * masking * projection
# remove drift
#ctod = tm.filter_median(ctod, length=3000 / 8.)
# first map
M = lo.aslinearoperator(model.aslinearoperator())
#P = lo.aslinearoperator(projection.aslinearoperator())
#C = csh.averaging(tod.shape, factor=8)
#I = lo.mask(uctod.mask)
#M = C * I.T * I * P
#M = C * P
backmap = model.transpose(ctod)
weights = model.transpose(ctod.ones(ctod.shape))
projection = pacs.get_projection_operator(resolution=3.2, npixels_per_sample=5) multiplexing = CompressionAverageOperator(1) crosstalk = IdentityOperator() compression = CompressionAverageOperator(4) model = compression * crosstalk * multiplexing * projection * telescope # read the Tod off the disk tod40Hz = pacs.get_tod(flatfielding=True, subtraction_mean=True) # remove drift tod40Hz_filtered = filter_polynomial(tod40Hz, 6) drift = tod40Hz - tod40Hz_filtered tod40Hz = filter_median(tod40Hz_filtered, 10000) # second level deglitching tod40Hz.mask = deglitch_l2mad(tod40Hz, projection, nsigma=5) idetector = 5 plot_tod((tod40Hz + drift)[idetector]) plot(drift[idetector], 'r') masking = MaskOperator(tod40Hz.mask) model40Hz = masking * projection map_naive40Hz = mapper_naive(tod40Hz, model40Hz) map_naive40Hz.imshow() # clim doesn't work anymore with AnnotatedImage #clim(-0.00002,0.00002)
fine_sampling_factor=1,
keep_bad_detectors=False)
# reset pacs header to have a shape multiple of 4
#header = pacs.get_map_header()
#header['NAXIS1'] = 192
#header['NAXIS2'] = 192
#header['CRPIX1'] = 96
#header['CRPIX2'] = 96
# data
tod = pacs.get_tod()
# remove bad pixels (by updating mask !)
#tod = remove_bad_pixels(tod)
# deglitching
#projection = tm.Projection(pacs, header=header, resolution=3., npixels_per_sample=6)
projection = tm.Projection(pacs, resolution=3., npixels_per_sample=6)
tm.deglitch_l2mad(tod, projection)
# model
masking = tm.Masking(tod.mask)
model = masking * projection
# remove drift
tod = tm.filter_median(tod, length=999)
model = masking * projection
# naive map
backmap = model.transpose(tod)
# coverage map
weights = model.transpose(tod.ones(tod.shape))
# mask on map
mask = weights == 0
M = lo.mask(mask)
# preconditionner