def MtX(self, x):
r"""Adjoint to degradation operator :func:`MX`."""
x = utils.reg_format(x * self.SNR_weights)
upsamp_x = np.array(
[nf * utils.adjoint_degradation_op(x_i, shift_ker, self.D) for
nf, x_i, shift_ker
in zip(self.normfacs, x, utils.reg_format(self.ker_rot))])
x, upsamp_x = utils.rca_format(x), utils.rca_format(upsamp_x)
return utils.apply_transform(upsamp_x.dot(self.A.T), self.filters)
def recombine(self, transf_S):
r"""Recombine new S and return it."""
S = np.array([
filter_convolve(transf_Sj, self.filters, filter_rot=True)
for transf_Sj in transf_S
])
return utils.rca_format(S).dot(self.A)
def update_H_glob(self, new_H_glob):
r"""Update current global model."""
self.H_glob = new_H_glob
dec_H_glob = np.array(
[nf * utils.degradation_op(H_i, shift_ker, self.D)
for nf, shift_ker, H_i in zip(self.normfacs,
utils.reg_format(self.ker),
utils.reg_format(self.H_glob))])
self.FdH_glob = utils.rca_format(dec_H_glob)
def update_H_loc(self, new_H_loc):
r"""Update current local models."""
self.H_loc = new_H_loc
dec_H_loc = np.array([nf * utils.degradation_op(H_i, shift_ker, self.D)
for nf, shift_ker, H_i in
zip(self.normfacs,
utils.reg_format(self.ker),
utils.reg_format(self.H_loc))])
self.FdH_loc = utils.rca_format(dec_H_loc)
def MX(self, transf_S):
r"""Apply degradation operator and renormalize.
Parameters
----------
transf_S : numpy.ndarray
Current eigenPSFs in Starlet space.
Returns
-------
numpy.ndarray result
"""
S = utils.rca_format(
np.array([filter_convolve(transf_Sj, self.filters, filter_rot=True)
for transf_Sj in transf_S]))
dec_rec = np.array(
[nf * utils.degradation_op(S.dot(A_i), shift_ker, self.D)
for nf, A_i, shift_ker in zip(self.normfacs,
self.A.T,
utils.reg_format(self.ker))])
self._current_rec = utils.rca_format(dec_rec)
return self._current_rec
def prep_mccd_inputs(self, starcat_array):
r"""Prepare the inputs for mccd algorithm.
Taks done:
- Translate from local to global coordinate system.
- Apply mask to stars.
- Normalize star values.
- Modify the star format.
Parameters
----------
starcat_array: numpy.ndarray
Array with (starcat_id, ccd_n, path) for every file in one
starcat_id (exposure ID).
Returns
-------
star_list: list
List containing the masked star stamps.
position_list: list
List containing the positions in the global coordinate system.
mask_list: list
List containing the masks corresponding to the star stamps.
ccd_list: list
List containing the CCD ids for the stars.
SNR_list: list
List containing the estimated SNR values for the stars.
Will be None if there are no SNR values available.
RA_list: list
List containing the RA coordinate for the stars.
Will be None if there are no RA coordinates available.
DEC_list: list
List containing the DEC coordinate for the stars.
Will be None if there are no DEC coordinates available.
"""
number_ccd = starcat_array.shape[0]
star_list = []
position_list = []
mask_list = []
ccd_list = []
SNR_list = []
RA_list = []
DEC_list = []
for it in range(number_ccd):
starcat = fits.open(starcat_array[it, 2])
ccd = starcat_array[it, 1].astype('int')
positions = np.array([
self.loc2glob.loc2glob_img_coord(ccd, x, y)
for x, y in zip(starcat[2].data[self.coord_x_descriptor],
starcat[2].data[self.coord_y_descriptor])
])
stars = utils.rca_format(starcat[2].data['VIGNET'])
masks = self.handle_mask(stars,
thresh=self.mask_thresh,
apply_to_stars=True)
star_list.append(stars)
position_list.append(positions)
mask_list.append(masks)
ccd_list.append(ccd)
try:
SNR = starcat[2].data['SNR_WIN']
SNR_list.append(SNR)
except Exception:
SNR_list = None
try:
RA_list.append(starcat[2].data['XWIN_WORLD'])
DEC_list.append(starcat[2].data['YWIN_WORLD'])
except Exception:
RA_list = None
DEC_list = None
self.SNR_list = SNR_list
self.star_list = star_list
self.position_list = position_list
self.mask_list = mask_list
self.ccd_list = ccd_list
self.RA_list = RA_list
self.DEC_list = DEC_list
return star_list, position_list, mask_list, ccd_list, SNR_list,\
RA_list, DEC_list