def thomson(data, cube, u=.5, **kwargs):
"""
Defines a Thomson scattering model for white light coronographs.
Parameters
----------
data: 3D InfoArray
Data stack.
cube: 3D FitsArray
Map cube.
Returns
-------
P : The projector with masking
D : Smoothness priors
obj_mask : object mask array
data_mask : data mask array
"""
# data mask
data_mask = solar.define_data_mask(data, **kwargs)
# projector
pb = kwargs.get('pb', 'pb')
if pb == 'pb':
P = pb_thomson_lo(data, cube, u, mask=data_mask)
else:
raise ValueError('Only pb implemented for now.')
# priors
D = [lo.diff(cube.shape, axis=i) for i in xrange(cube.ndim)]
# masks
kwargs['remove_nan'] = True
P, D, obj_mask = _apply_object_mask(P, D, cube, **kwargs)
return P, D, obj_mask, data_mask
def stsrt(data, cube, **kwargs):
"""
Smooth Temporal Solar Rotational Tomography.
Assumes data is sorted by observation time 'DATE_OBS'.
Returns
-------
P : The projector with masking
D : Smoothness priors
obj_mask : object mask array
data_mask : data mask array
"""
# Parse kwargs.
obj_rmin = kwargs.get('obj_rmin', None)
obj_rmax = kwargs.get('obj_rmax', None)
# mask data
data_mask = solar.define_data_mask(data, **kwargs)
# define temporal groups
times = [solar.convert_time(h['DATE_OBS']) for h in data.header]
## if no interval is given separate every image
dt_min = kwargs.get('dt_min', np.max(np.diff(times)) + 1)
#groups = solar.temporal_groups(data, dt_min)
ind = solar.temporal_groups_indexes(data, dt_min)
n = len(ind)
# define new 4D cube
cube4 = cube[..., np.newaxis].repeat(n, axis=-1)
cube4.header = copy.copy(cube.header)
cube4.header['NAXIS'] = 4
cube4.header['NAXIS4'] = cube4.shape[3]
# define 4d model
# XXX assumes all groups have same number of elements
ng = data.shape[-1] / n
P = siddon4d_lo(data.header, cube4.header, ng=ng, mask=data_mask, obstacle="sun")
# priors
D = smoothness_prior(cube4, kwargs.get("height_prior", False))
# 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[..., np.newaxis].repeat(n, axis=-1)
if kwargs.get("decimate", False) or kwargs.get("remove_nan", False):
Mo = lo.decimate(obj_mask)
else:
Mo = lo.ndmask(obj_mask)
P = P * Mo.T
D = [Di * Mo.T for Di in D]
else:
obj_mask = None
return P, D, obj_mask, data_mask
def _apply_data_mask(P, data, **kwargs):
# Parse kwargs.
data_rmin = kwargs.get('data_rmin', None)
data_rmax = kwargs.get('data_rmax', None)
mask_negative = kwargs.get('mask_negative', False)
mask_nan = kwargs.get('mask_nan', True)
# define mask if required
if (data_rmin is not None or data_rmax is not None or
mask_negative is not False or mask_nan is not False):
data_mask = solar.define_data_mask(data, **kwargs)
Md = lo.mdmask(data_mask)
# apply mask to model
P = Md * P
else:
data_mask = None
return P, data_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
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
