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
def pb_thomson_lo(data, in_map, u, mask=None):
"""Defines thomson scattering linear operator"""
# data coefs
data_coefs = _pb_data_coef(data).flatten()
O = lo.diag(data_coefs)
# map coefs
map_coefs = _pb_map_coef(in_map, u).flatten()
M = lo.diag(map_coefs)
# projection
P = siddon_lo(data.header, in_map.header, obstacle="sun", mask=mask)
# thomson lo
T = O * P * M
return T
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
obj_mask : object mask array
data_mask : data mask array
"""
# Model : it is Solar rotational tomography, so obstacle="sun".
data_mask = solar.define_data_mask(data, **kwargs)
P = siddon_lo(data.header, cube.header, mask=data_mask, obstacle="sun")
D = smoothness_prior(cube, kwargs.get("height_prior", False))
P, D, obj_mask = _apply_object_mask(P, D, cube, **kwargs)
return P, D, obj_mask, data_mask
