def stsrt(data, cube, **kwargs):
"""
Smooth Temporal Solar Rotational Tomography.
Assumes data is sorted by observation time 'DATE_OBS'.
"""
# Parse kwargs.
obj_rmin = kwargs.get('obj_rmin', None)
obj_rmax = kwargs.get('obj_rmax', None)
data_rmin = kwargs.get('data_rmin', None)
data_rmax = kwargs.get('data_rmax', None)
mask_negative = kwargs.get('mask_negative', False)
# define temporal groups
times = [secchi.convert_time(t) for t in data.header['DATE_OBS']]
## if no interval is given separate every image
dt_min = kwargs.get('dt_min', np.max(np.diff(times)) + 1)
#groups = secchi.temporal_groups(data, dt_min)
ind = secchi.temporal_groups_indexes(data, dt_min)
n = len(ind)
# 4d model
cube_header = cube.header.copy()
cube_header.update('NAXIS', 4)
cube_header.update('NAXIS4', data.shape[-1])
P = siddon4d_lo(data.header, cube_header, obstacle="sun")
# define per group summation of maps
# define new 4D cube
cube4 = cube.reshape(cube.shape + (1,)).repeat(n, axis=-1)
cube4.header.update('NAXIS', 4)
cube4.header.update('NAXIS4', cube4.shape[3])
cube4.header.update('CRVAL4', 0.)
cube4.header.update('CDELT4', dt_min)
S = group_sum(ind, cube, data)
P = P * S.T
# priors
D = [lo.diff(cube4.shape, axis=i) for i in xrange(cube.ndim)]
# mask object
if obj_rmin is not None or obj_rmax is not None:
Mo, obj_mask = mask_object(cube, kwargs)
obj_mask = obj_mask.reshape(obj_mask.shape + (1,)).repeat(n, axis=-1)
Mo = lo.mask(obj_mask)
P = P * Mo.T
D = [Di * Mo.T for Di in D]
else:
obj_mask = None
# mask data
if (data_rmin is not None or
data_rmax is not None or
mask_negative is not None):
data_mask = secchi.define_data_mask(data,
Rmin=data_rmin,
Rmax=data_rmax,
mask_negative=True)
Md = lo.mask(data_mask)
P = Md * P
else:
data_mask = None
return P, D, obj_mask, data_mask, cube4
def mask_object(cube, kwargs):
obj_rmin = kwargs.get('obj_rmin', None)
obj_rmax = kwargs.get('obj_rmax', None)
if obj_rmin is not None or obj_rmax is not None:
obj_mask = secchi.define_map_mask(cube,
Rmin=obj_rmin,
Rmax=obj_rmax)
Mo = lo.mask(obj_mask)
return Mo, obj_mask
def srt(data, cube, **kwargs):
"""
Define Solar Rotational Tomography model with optional masking of
data and map areas. Can also define priors.
Parameters
----------
data: InfoArray
data cube
cube: FitsArray
map cube
obj_rmin: float
Object minimal radius. Areas below obj_rmin are masked out.
obj_rmax: float
Object maximal radius. Areas above obj_rmax are masked out.
data_rmin: float
Data minimal radius. Areas below data_rmin are masked out.
data_rmax: float
Data maximal radius. Areas above data_rmax are masked out.
mask_negative: boolean
If true, negative values in the data are masked out.
Returns
-------
P : The projector with masking
D : Smoothness priors
"""
# Parse kwargs.
obj_rmin = kwargs.get('obj_rmin', None)
obj_rmax = kwargs.get('obj_rmax', None)
data_rmin = kwargs.get('data_rmin', None)
data_rmax = kwargs.get('data_rmax', None)
mask_negative = kwargs.get('mask_negative', None)
# Model : it is Solar rotational tomography, so obstacle="sun".
P = siddon_lo(data.header, cube.header, obstacle="sun")
D = [lo.diff(cube.shape, axis=i) for i in xrange(cube.ndim)]
# Define masking.
if obj_rmin is not None or obj_rmax is not None:
Mo, obj_mask = mask_object(cube, kwargs)
P = P * Mo.T
D = [Di * Mo.T for Di in D]
else:
obj_mask = None
if (data_rmin is not None or
data_rmax is not None or
mask_negative is not None):
data_mask = secchi.define_data_mask(data,
Rmin=data_rmin,
Rmax=data_rmax,
mask_negative=True)
Md = lo.mask(data_mask)
P = Md * P
else:
data_mask = None
return P, D, obj_mask, data_mask
import lo.pywt_lo as pywt_lo
# data
pacs = PacsObservation(filename=tamasis_dir + 'tests/frames_blue.fits',
fine_sampling_factor=1,
keep_bad_detectors=False)
tod = pacs.get_tod()
# projector
projection = Projection(pacs,
resolution=3.2,
oversampling=False,
npixels_per_sample=6)
model = projection
# naive map
backmap = model.transpose(tod)
# mask
M = lo.mask(backmap < 0).T
#M = lo.identity(2 * (backmap.size,))
# transform to lo
P = lo.aslinearoperator(model.aslinearoperator()) * M
# prior
Dx = lo.diff(backmap.shape, axis=0, dtype=np.float64) * M
Dy = lo.diff(backmap.shape, axis=1, dtype=np.float64) * M
Dw = pywt_lo.wavelet2(backmap.shape, "haar") * M
# inversion
y = tod.flatten()
callback = lo.CallbackFactory(verbose=True)
x = it.rirls(P, (Dx, Dy, Dw), y, p=1.5, maxiter=100, callback=callback)
sol = backmap.zeros(backmap.shape)
sol[:] = (M * x).reshape(sol.shape)
import numpy as np
import lo
import lo.iterative as it
import lo.pywt_lo as pywt_lo
# data
pacs = PacsObservation(filename=tamasis_dir+'tests/frames_blue.fits',
fine_sampling_factor=1,
keep_bad_detectors=False)
tod = pacs.get_tod()
# projector
projection = Projection(pacs, resolution=3.2, oversampling=False, npixels_per_sample=6)
model = projection
# naive map
backmap = model.transpose(tod)
# mask
M = lo.mask(backmap < 0).T
#M = lo.identity(2 * (backmap.size,))
# transform to lo
P = lo.aslinearoperator(model.aslinearoperator()) * M
# prior
Dx = lo.diff(backmap.shape, axis=0, dtype=np.float64) * M
Dy = lo.diff(backmap.shape, axis=1, dtype=np.float64) * M
Dw = pywt_lo.wavelet2(backmap.shape, "haar") * M
# inversion
y = tod.flatten()
callback = lo.CallbackFactory(verbose=True)
x = it.rirls(P, (Dx ,Dy, Dw), y, p=1.5, maxiter=100, callback=callback)
sol = backmap.zeros(backmap.shape)
sol[:] = (M * x).reshape(sol.shape)
# ctod = compression.direct(uctod)
# model
masking = tm.Masking(uctod.mask)
model = masking * projection
# remove drift
# ctod = tm.filter_median(ctod, length=3000 / 8.)
# first map
M = C * 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 = (M.T * ctod.flatten()).reshape(projection.shapein)
# weights = (M.T * np.ones(ctod.size)).reshape(projection.shapein)
weights = projection.transpose(tod.ones(tod.shape))
MM = lo.mask(weights == 0)
M = M * MM.T
# define algo
# priors
Dx = lo.diff(backmap.shape, axis=0, dtype=np.float64) * MM.T
Dy = lo.diff(backmap.shape, axis=1, dtype=np.float64) * MM.T
# Dw = lo.pywt_lo.wavedec2(backmap.shape, "haar", level=3)
# inversion
x, conv = lo.rls(M, (Dx, Dy), (1e0, 1e0), ctod.flatten())
sol = tm.Map(np.zeros(backmap.shape))
sol[:] = (MM.T * x).reshape(sol.shape)
sol.header = header
# save
sol.writefits(os.path.join(os.getenv("HOME"), "data", "csh", "output", "ngc6946_cs_rls.fits"))
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 iweights = 1 / weights iweights[np.where(np.isfinite(iweights) == 0)] = 0. M0 = lo.diag(iweights.flatten()) # transform to lo P = lo.aslinearoperator(model.aslinearoperator()) # priors Dx = lo.diff(backmap.shape, axis=0) Dy = lo.diff(backmap.shape, axis=1) # inversion y = (masking.T * tod).flatten() # algos algos = [spl.cg, spl.cgs, spl.bicg, spl.bicgstab] models = [P.T * P, P.T * P + Dx.T * Dx + Dy.T * Dy,] n_iterations = []
keep_bad_detectors=False) tod = pacs.get_tod() # compression model C = lo.binning(tod.shape, factor=8, axis=1, dtype=np.float64) # compress data ctod = C * tod.flatten() # projector projection = Projection(pacs, resolution=3.2, oversampling=False, npixels_per_sample=6) model = 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) # transform to lo #P = lo.ndsubclass(backmap, tod, matvec=model.direct, rmatvec=model.transpose) P = lo.aslinearoperator(model.aslinearoperator()) # full model A = C * P * M.T # priors Dx = lo.diff(backmap.shape, axis=0, dtype=np.float64) * M.T Dy = lo.diff(backmap.shape, axis=1, dtype=np.float64) * M.T Dw = lo.pywt_lo.wavelet2(backmap.shape, "haar") * M.T # inversion y = ctod.flatten() x, conv = lo.rls(A, (Dx, Dy, Dw), (1e1, 1e1, 1e1), y) sol = backmap.zeros(backmap.shape) sol[mask == 0] = x
header=header,
resolution=resolution,
npixels_per_sample=5)
deglitch_l2mad(tod, projection)
# model
masking = Masking(tod.mask)
model = masking * projection
# remove drift
#tod = filter_median(tod, length=3000)
# first map
backmap = model.transpose(tod)
# coverage
weights = model.transpose(tod.ones(tod.shape))
P = lo.aslinearoperator(model.aslinearoperator())
# mask not seen part of the map
MM = lo.mask(weights == 0)
y = tod.flatten()
# define algo
# priors
Dx = lo.diff(backmap.shape, axis=0, dtype=np.float64)
Dy = lo.diff(backmap.shape, axis=1, dtype=np.float64)
#Dw = lo.pywt_lo.wavedec2(backmap.shape, "haar", level=3)
# inversion
x, conv = lo.rls(P * MM.T, (Dx * MM.T, Dy * MM.T), (1e1, 1e1), y)
sol = backmap.zeros(backmap.shape)
sol[:] = (MM.T * x).reshape(sol.shape)
# save
sol.header = header
sol.writefits(
os.path.join(os.getenv('HOME'), 'data', 'csh', 'output',
'ngc6946_rls.fits'))