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