def setUp(self, shape=(7, 5, 5)):
"""Load a dataset for reconstruction."""
self.operator = Alignment()
self.operator.__enter__()
self.xp = self.operator.xp
padded_shape = shape + np.asarray((0, 41, 32))
flow = (self.xp.random.rand(*padded_shape, 2, dtype='float32') -
0.5) * 9
np.random.seed(0)
self.m = self.xp.asarray(random_complex(*shape), dtype='complex64')
self.m_name = 'unpadded'
self.d = self.xp.asarray(random_complex(*padded_shape),
dtype='complex64')
self.d_name = 'rotated'
self.kwargs = {
'flow': flow,
'padded_shape': padded_shape,
'unpadded_shape': shape,
'angle': np.random.rand() * 2 * np.pi,
'cval': 0,
}
print(self.operator)
class TestAlignment(unittest.TestCase, OperatorTests):
"""Test the Alignment operator."""
def setUp(self, shape=(7, 5, 5)):
"""Load a dataset for reconstruction."""
self.operator = Alignment()
self.operator.__enter__()
self.xp = self.operator.xp
padded_shape = shape + np.asarray((0, 41, 32))
flow = (self.xp.random.rand(*padded_shape, 2, dtype='float32') -
0.5) * 9
np.random.seed(0)
self.m = self.xp.asarray(random_complex(*shape), dtype='complex64')
self.m_name = 'unpadded'
self.d = self.xp.asarray(random_complex(*padded_shape),
dtype='complex64')
self.d_name = 'rotated'
self.kwargs = {
'flow': flow,
'padded_shape': padded_shape,
'unpadded_shape': shape,
'angle': np.random.rand() * 2 * np.pi,
'cval': 0,
}
print(self.operator)
@unittest.skip('FIXME: This operator is not scaled.')
def test_scaled(self):
pass
def invert(
original,
**kwargs
): # yapf: disable
"""Return original shifted by shift."""
with Alignment() as operator:
for key, value in kwargs.items():
if not isinstance(value, tuple) and np.ndim(value) > 0:
kwargs[key] = operator.asarray(value)
unaligned = operator.inv(
operator.asarray(original, dtype='complex64'),
**kwargs,
)
assert unaligned.dtype == 'complex64', unaligned.dtype
return operator.asnumpy(unaligned)
def reconstruct(
original,
unaligned,
algorithm,
num_iter=1, rtol=-1, **kwargs
): # yapf: disable
"""Solve the alignment problem; returning either the original or the shift.
Parameters
----------
unaligned, original: (..., H, W) complex64
The images to be aligned.
rtol : float
Terminate early if the relative decrease of the cost function is
less than this amount.
"""
if algorithm in solvers.__all__:
with Alignment() as operator:
for key, value in kwargs.items():
if not isinstance(value, tuple) and np.ndim(value) > 0:
kwargs[key] = operator.asarray(value)
logger.info("{} on {:,d} - {:,d} by {:,d} images for {:,d} "
"iterations.".format(algorithm, *unaligned.shape,
num_iter))
result = getattr(solvers, algorithm)(
operator,
original=operator.asarray(original, dtype='complex64'),
unaligned=operator.asarray(unaligned, dtype='complex64'),
num_iter=num_iter,
**kwargs,
)
return {k: operator.asnumpy(v) for k, v in result.items()}
else:
raise ValueError(
"The '{}' algorithm is not an available.".format(algorithm))