def test_discrete_gradient_cuda():
"""Discretized spatial gradient operator using CUDA."""
# Check result of operator with explicit summation
# phantom data
data = np.array([[0., 1., 2., 3., 4.],
[1., 2., 3., 4., 5.],
[2., 3., 4., 5., 6.]])
# DiscreteLp Vector
discr_space = uniform_discr([0, 0], [6, 2.5], data.shape, impl='cuda')
dom_vec = discr_space.element(data)
# computation of gradient components with helper function
dx0, dx1 = discr_space.grid.stride
df0 = finite_diff(data, axis=0, dx=dx0, zero_padding=True, edge_order=2)
df1 = finite_diff(data, axis=1, dx=dx1, zero_padding=True, edge_order=2)
# gradient
grad = DiscreteGradient(discr_space)
grad_vec = grad(dom_vec)
assert len(grad_vec) == data.ndim
assert all_equal(grad_vec[0].asarray(), df0)
assert all_equal(grad_vec[1].asarray(), df1)
# adjoint operator
ran_vec = grad.range.element([data, data ** 2])
adj_vec = grad.adjoint(ran_vec)
lhs = ran_vec.inner(grad_vec)
rhs = dom_vec.inner(adj_vec)
assert lhs != 0
assert rhs != 0
assert lhs == rhs
def test_discrete_gradient():
"""Discretized spatial gradient operator."""
discr_space = Rn(1)
with pytest.raises(TypeError):
DiscreteGradient(discr_space)
# Check result of operator with explicit summation
# phantom data
data = np.array([[0., 1., 2., 3., 4.],
[1., 2., 3., 4., 5.],
[2., 3., 4., 5., 6.]])
data = np.array([[0., 1., 2., 3., 4.],
[0., 1., 2., 3., 4.],
[0., 1., 2., 3., 4.]])
# DiscreteLp Vector
discr_space = uniform_discr([0, 0], [6, 2.5], data.shape)
dom_vec = discr_space.element(data)
# computation of gradient components with helper function
dx0, dx1 = discr_space.grid.stride
df0 = finite_diff(data, axis=0, dx=dx0, zero_padding=True, edge_order=2)
df1 = finite_diff(data, axis=1, dx=dx1, zero_padding=True, edge_order=2)
# gradient
grad = DiscreteGradient(discr_space)
grad_vec = grad(dom_vec)
assert len(grad_vec) == data.ndim
assert all_equal(grad_vec[0].asarray(), df0)
assert all_equal(grad_vec[1].asarray(), df1)
# adjoint operator
ran_vec = grad.range.element([data, data ** 2])
adj_vec = grad.adjoint(ran_vec)
lhs = ran_vec.inner(grad_vec)
rhs = dom_vec.inner(adj_vec)
assert lhs != 0
assert rhs != 0
assert lhs == rhs
# higher dimensional arrays
lin_size = 3
for ndim in range(1, 6):
# DiscreteLp Vector
discr_space = uniform_discr([0.] * ndim, [lin_size] * ndim,
[lin_size] * ndim)
dom_vec = discr_space.element(ndvolume(lin_size, ndim))
# gradient
grad = DiscreteGradient(discr_space)
grad(dom_vec)