def _op_method(self, input_data, extra_factor=1.0, rank=None):
"""Operator.
This method returns the input data after the singular values have been
thresholded.
Parameters
----------
input_data : numpy.ndarray
Input data array
extra_factor : float
Additional multiplication factor (default is ``1.0``)
rank: int, optional
Estimation of the rank to save computation time in standard mode,
if not set an internal estimation is used.
Returns
-------
numpy.ndarray
SVD thresholded data
Raises
------
ValueError
if lowr_type is not in ``{'standard', 'ngole'}``
"""
# Update threshold with extra factor.
threshold = self.thresh * extra_factor
if self.lowr_type == 'standard' and self.rank is None and rank is None:
data_matrix = svd_thresh(
cube2matrix(input_data),
threshold,
thresh_type=self.thresh_type,
)
elif self.lowr_type == 'standard':
data_matrix, update_rank = svd_thresh_coef_fast(
cube2matrix(input_data),
threshold,
n_vals=rank or self.rank,
extra_vals=5,
thresh_type=self.thresh_type,
)
self.rank = update_rank # save for future use
elif self.lowr_type == 'ngole':
data_matrix = svd_thresh_coef(
cube2matrix(input_data),
self.operator,
threshold,
thresh_type=self.thresh_type,
)
else:
raise ValueError('lowr_type should be standard or ngole')
# Return updated data.
return matrix2cube(data_matrix, input_data.shape[1:])
def test_cube2matrix(self):
"""Test cube2matrix."""
npt.assert_array_equal(
transform.cube2matrix(self.cube),
self.matrix,
err_msg='Incorrect transformation: cube2matrix',
)
def _cost_method(self, *args, **kwargs):
"""Calculate low-rank component of the cost.
This method returns the nuclear norm error of the deconvolved data in
matrix form.
Parameters
----------
args : interable
Positional arguments
kwargs : dict
Keyword arguments
Returns
-------
float
Low-rank cost component
"""
cost_val = self.thresh * nuclear_norm(cube2matrix(args[0]))
if 'verbose' in kwargs and kwargs['verbose']:
print(' - NUCLEAR NORM (X):', cost_val)
return cost_val
def _op_method(self, input_data, extra_factor=1.0):
"""Operator.
This method returns the input data after the singular values have been
thresholded.
Parameters
----------
input_data : numpy.ndarray
Input data array
extra_factor : float
Additional multiplication factor (default is ``1.0``)
Returns
-------
numpy.ndarray
SVD thresholded data
"""
# Update threshold with extra factor.
threshold = self.thresh * extra_factor
if self.lowr_type == 'standard':
data_matrix = svd_thresh(
cube2matrix(input_data),
threshold,
thresh_type=self.thresh_type,
)
elif self.lowr_type == 'ngole':
data_matrix = svd_thresh_coef(
cube2matrix(input_data),
self.operator,
threshold,
thresh_type=self.thresh_type,
)
# Return updated data.
return matrix2cube(data_matrix, input_data.shape[1:])
def _cost_method(self, *args, **kwargs):
"""Calculate low-rank component of the cost
This method returns the nuclear norm error of the deconvolved data in
matrix form
Returns
-------
float low-rank cost component
"""
cost_val = self.thresh * nuclear_norm(cube2matrix(args[0]))
if 'verbose' in kwargs and kwargs['verbose']:
print(' - NUCLEAR NORM (X):', cost_val)
return cost_val