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 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
