def test_full_transform_scalar(self):
"""Test extracting the full transform when only a scalar is provided.
"""
# Construct the expected matrix
expected = np.zeros((4, 8))
expected[np.array([1, 2, 3]), np.array([5, 6, 7])] = 1.5
# Create the transform
t = Transform(size_in=8, slice_in=slice(5, None),
transform=1.5,
size_out=4, slice_out=[1, 2, 3])
# Check the full transform is correct
assert np.array_equal(
t.full_transform(slice_in=True, slice_out=False),
expected[:, 5:]
)
assert np.array_equal(
t.full_transform(slice_in=False, slice_out=True),
expected[1:]
)
assert np.array_equal(
t.full_transform(slice_in=False, slice_out=False),
expected
)
def test_full_transform_matrix(self):
"""Test extracting the full transform when a matrix is provided.
"""
# Construct the expected matrix
expected = np.zeros((4, 8))
matrix = np.arange(9)
matrix.shape = (3, 3)
expected[:3, 3:6] = matrix
# Create the transform
t = Transform(size_in=8, slice_in=[3, 4, 5],
transform=matrix,
size_out=4, slice_out=slice(3))
# Check the full transform is correct
assert np.array_equal(
t.full_transform(slice_in=True, slice_out=False),
expected[:, 3:6]
)
assert np.array_equal(
t.full_transform(slice_in=False, slice_out=True),
expected[:3]
)
assert np.array_equal(
t.full_transform(slice_in=False, slice_out=False),
expected
)
def test_full_transform_matrix(self):
"""Test extracting the full transform when a matrix is provided.
"""
# Construct the expected matrix
expected = np.zeros((4, 8))
matrix = np.arange(9)
matrix.shape = (3, 3)
expected[:3, 3:6] = matrix
# Create the transform
t = Transform(size_in=8,
slice_in=[3, 4, 5],
transform=matrix,
size_out=4,
slice_out=slice(3))
# Check the full transform is correct
assert np.array_equal(t.full_transform(slice_in=True, slice_out=False),
expected[:, 3:6])
assert np.array_equal(t.full_transform(slice_in=False, slice_out=True),
expected[:3])
assert np.array_equal(
t.full_transform(slice_in=False, slice_out=False), expected)
def test_full_transform_vector(self):
"""Test extracting the full transform when only a vector is provided.
"""
# Construct the expected matrix
expected = np.zeros((4, 8))
diag = np.array([1.0, 2.0, 3.0])
expected[np.array([0, 1, 2]), np.array([3, 4, 5])] = diag
# Create the transform
t = Transform(size_in=8, slice_in=[3, 4, 5],
transform=diag,
size_out=4, slice_out=slice(3))
# Check the full transform is correct
assert np.array_equal(
t.full_transform(slice_in=True, slice_out=False),
expected[:, 3:6]
)
assert np.array_equal(
t.full_transform(slice_in=False, slice_out=True),
expected[:3]
)
assert np.array_equal(
t.full_transform(slice_in=False, slice_out=False),
expected
)
def test_concat_empty_transform_zero_transform_trivial(self):
# Build the transforms
A = Transform(size_in=2, transform=0, size_out=2)
B = Transform(size_in=2, transform=1, size_out=2)
# Check that the test is correct
expected = np.dot(B.full_transform(False, False),
A.full_transform(False, False))
assert not np.any(expected), "Test broken"
# Combine the transforms, this should return None to indicate that the
# transform is empty.
assert A.concat(B) is None
def test_concat_empty_transform_mismatched_slicing(self):
# Build the transforms
A = Transform(size_in=1, transform=1, size_out=2, slice_out=[0])
B = Transform(size_in=2, slice_in=[1], transform=1, size_out=1)
# Check that the test is correct
expected = np.dot(B.full_transform(False, False),
A.full_transform(False, False))
assert not np.any(expected), "Test broken"
# Combine the transforms, this should return None to indicate that the
# transform is empty.
assert A.concat(B) is None
def test_concat_empty_transform_zero_transform_trivial(self):
# Build the transforms
A = Transform(size_in=2, transform=0, size_out=2)
B = Transform(size_in=2, transform=1, size_out=2)
# Check that the test is correct
expected = np.dot(B.full_transform(False, False),
A.full_transform(False, False))
assert not np.any(expected), "Test broken"
# Combine the transforms, this should return None to indicate that the
# transform is empty.
assert A.concat(B) is None
def test_concat_empty_transform_mismatched_slicing(self):
# Build the transforms
A = Transform(size_in=1, transform=1, size_out=2, slice_out=[0])
B = Transform(size_in=2, slice_in=[1], transform=1, size_out=1)
# Check that the test is correct
expected = np.dot(B.full_transform(False, False),
A.full_transform(False, False))
assert not np.any(expected), "Test broken"
# Combine the transforms, this should return None to indicate that the
# transform is empty.
assert A.concat(B) is None
def test_concat(self, a_params, b_params):
# Build the transforms
A = Transform(size_in=8, size_out=8, **a_params)
B = Transform(size_in=8, size_out=4, **b_params)
# Compute the expected combined transform
expected = np.dot(B.full_transform(False, False),
A.full_transform(False, False))
# Combine the transforms
C = A.concat(B)
# Test
assert np.array_equal(expected, C.full_transform(False, False))
def test_concat(self, a_params, b_params):
# Build the transforms
A = Transform(size_in=8, size_out=8, **a_params)
B = Transform(size_in=8, size_out=4, **b_params)
# Compute the expected combined transform
expected = np.dot(B.full_transform(False, False),
A.full_transform(False, False))
# Combine the transforms
C = A.concat(B)
# Test
assert np.array_equal(expected, C.full_transform(False, False))
def test_full_transform_scalar(self):
"""Test extracting the full transform when only a scalar is provided.
"""
# Construct the expected matrix
expected = np.zeros((4, 8))
expected[np.array([1, 2, 3]), np.array([5, 6, 7])] = 1.5
# Create the transform
t = Transform(size_in=8,
slice_in=slice(5, None),
transform=1.5,
size_out=4,
slice_out=[1, 2, 3])
# Check the full transform is correct
assert np.array_equal(t.full_transform(slice_in=True, slice_out=False),
expected[:, 5:])
assert np.array_equal(t.full_transform(slice_in=False, slice_out=True),
expected[1:])
assert np.array_equal(
t.full_transform(slice_in=False, slice_out=False), expected)
def test_full_transform_vector(self):
"""Test extracting the full transform when only a vector is provided.
"""
# Construct the expected matrix
expected = np.zeros((4, 8))
diag = np.array([1.0, 2.0, 3.0])
expected[np.array([0, 1, 2]), np.array([3, 4, 5])] = diag
# Create the transform
t = Transform(size_in=8,
slice_in=[3, 4, 5],
transform=diag,
size_out=4,
slice_out=slice(3))
# Check the full transform is correct
assert np.array_equal(t.full_transform(slice_in=True, slice_out=False),
expected[:, 3:6])
assert np.array_equal(t.full_transform(slice_in=False, slice_out=True),
expected[:3])
assert np.array_equal(
t.full_transform(slice_in=False, slice_out=False), expected)