def mask_object(cube, decimate=False, remove_nan=False, **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 = solar.define_map_mask(cube, **kwargs)
# decimate is mandatory to remove nan because NaN * 0 = NaN
if decimate or remove_nan:
Mo = lo.decimate(obj_mask, dtype=cube.dtype)
else:
Mo = lo.ndmask(obj_mask, dtype=cube.dtype)
return Mo, obj_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 check_model(model, im_h, obj_h):
obj = siddon.simu.object_from_header(obj_h)
obj = siddon.fa.InfoArray(data=obj, header=dict(obj.header))
obj[:] = 1.
im_h['n_images'] = 100
im_h['max_lon'] = 3 * np.pi
data = siddon.simu.circular_trajectory_data(**im_h)
if obj.dtype == data.dtype:
P, D, obj_mask, data_mask = model(data,
obj,
obj_rmin=1.,
decimate=True)
Mo = lo.decimate(obj_mask)
w = (Mo.T * (P.T * np.ones(data.size))).reshape(obj_mask.shape)
is_seen = (w != 0)
new_obj = fa.FitsArray(obj_mask.shape)
new_obj = 1.
new_obj *= (1 - obj_mask)
data[:] = (P * Mo * new_obj.ravel()).reshape(data.shape)
hypers = new_obj.ndim * (1e-10, )
sol = lo.acg(P, data.ravel(), D, hypers=hypers, tol=1e-20)
sol = fa.asfitsarray((Mo.T * sol).reshape(obj_mask.shape),
header=obj.header)
assert_almost_equal(sol[is_seen], new_obj[is_seen], decimal=1)
# define masking of coefficients
# ------------------------------
# number of coefficients to keep
factor = 8.
nk = P.shape[0] / factor
# coverage map
w = P.T * np.ones(P.shape[0])
# sort coverage map coefficients
ws = np.sort(w)
# find the nk largest coverage elements
threshold = ws[-nk]
mask = w < threshold
# define decimation Linear Operator according to coverage map coef
# selection.
M = lo.decimate(mask)
# model is projection matrix time decimation matrix
H = P * M.T
# Store model matrix on hard-drive as FITS
# ---------------------------------------------
# convert LinearOperator to dense matrix (ndarray)
Hd = H.todense()
Hd = np.asmatrix(Hd)
# convert into my FitsArray :
Hd_fits = fa.FitsArray(data=Hd)
# save the model to defined filename
filename = os.path.join(os.getenv("HOME"), "data", "pacs",
"mmc_model_cam_angle_0_scan_angle_0_speed60.fits")
Hd_fits.tofits(filename)
# define masking of coefficients
# ------------------------------
# number of coefficients to keep
factor = 8.
nk = P.shape[0] / factor
# coverage map
w = P.T * np.ones(P.shape[0])
# sort coverage map coefficients
ws = np.sort(w)
# find the nk largest coverage elements
threshold = ws[-nk]
mask = w < threshold
# define decimation Linear Operator according to coverage map coef
# selection.
M = lo.decimate(mask)
# model is projection matrix time decimation matrix
H = P * M.T
# Store model matrix on hard-drive as FITS
# ---------------------------------------------
# convert LinearOperator to dense matrix (ndarray)
Hd = H.todense()
Hd = np.asmatrix(Hd)
# convert into my FitsArray :
Hd_fits = fa.FitsArray(data=Hd)
# save the model to defined filename
filename = os.path.join(os.getenv("HOME"), "data", "pacs",
"mmc_model_cam_angle_0_scan_angle_0_speed60.fits")
Hd_fits.tofits(filename)