def __init__(self, operator, trans_operator, shape,
lips_calc_max_iter=10, lipschitz_cst=None, num_check_lips=10,
verbose=0):
# Initialize the GradBase with dummy data
super(GradBaseMRI, self).__init__(
np.array(0),
operator,
trans_operator,
)
if lipschitz_cst is not None:
self.spec_rad = lipschitz_cst
self.inv_spec_rad = 1.0 / self.spec_rad
else:
calc_lips = PowerMethod(self.trans_op_op, shape,
data_type=np.complex, auto_run=False)
calc_lips.get_spec_rad(extra_factor=1.1,
max_iter=lips_calc_max_iter)
self.spec_rad = calc_lips.spec_rad
self.inv_spec_rad = calc_lips.inv_spec_rad
if verbose > 0:
print("Lipschitz constant is " + str(self.spec_rad))
if num_check_lips > 0:
is_lips = check_lipschitz_cst(f=self.trans_op_op,
x_shape=shape,
lipschitz_cst=self.spec_rad,
max_nb_of_iter=num_check_lips)
if not is_lips:
raise ValueError('The lipschitz constraint is not satisfied')
else:
if verbose > 0:
print('The lipschitz constraint is satisfied')
def update_S(self, new_S, update_spectral_radius=True):
r"""Update current eigenPSFs."""
self.S = new_S
self.FdS = np.array([[nf * utils.degradation_op(S_j, shift_ker, self.D)
for nf, shift_ker in
zip(self.normfacs, utils.reg_format(self.ker))]
for S_j in utils.reg_format(self.S)])
if update_spectral_radius:
PowerMethod.get_spec_rad(self)
def update_S(self, new_S, update_spectral_radius=True):
""" Update current eigenPSFs."""
self.S = new_S
# Apply degradation operator to components
normfacs = self.flux / (np.median(self.flux) * self.sig)
self.FdS = np.array([[
nf * degradation_op(S_j, shift_ker, self.D)
for nf, shift_ker in zip(normfacs, utils.reg_format(self.ker))
] for S_j in utils.reg_format(self.S)])
if update_spectral_radius:
PowerMethod.get_spec_rad(self)
def update_A(self, new_A, update_spectral_radius=True):
r"""Update current coefficients."""
self.A = new_A
if update_spectral_radius:
PowerMethod.get_spec_rad(self)