def test_gradient(space, method, padding):
"""Discretized spatial gradient operator."""
places = 2 if space.dtype == np.float32 else 4
with pytest.raises(TypeError):
Gradient(odl.rn(1), method=method)
if isinstance(padding, tuple):
pad_mode, pad_const = padding
else:
pad_mode, pad_const = padding, 0
# DiscreteLp Vector
dom_vec = noise_element(space)
dom_vec_arr = dom_vec.asarray()
# gradient
grad = Gradient(space,
method=method,
pad_mode=pad_mode,
pad_const=pad_const)
grad_vec = grad(dom_vec)
assert len(grad_vec) == space.ndim
# computation of gradient components with helper function
for axis, dx in enumerate(space.cell_sides):
diff = finite_diff(dom_vec_arr,
axis=axis,
dx=dx,
method=method,
pad_mode=pad_mode,
pad_const=pad_const)
assert all_almost_equal(grad_vec[axis].asarray(), diff)
# Test adjoint operator
derivative = grad.derivative()
ran_vec = noise_element(derivative.range)
deriv_grad_vec = derivative(dom_vec)
adj_grad_vec = derivative.adjoint(ran_vec)
lhs = ran_vec.inner(deriv_grad_vec)
rhs = dom_vec.inner(adj_grad_vec)
# Check not to use trivial data
assert lhs != 0
assert rhs != 0
assert almost_equal(lhs, rhs, places=places)
# Higher-dimensional arrays
lin_size = 3
for ndim in [1, 3, 6]:
space = odl.uniform_discr([0.] * ndim, [1.] * ndim, [lin_size] * ndim)
dom_vec = odl.phantom.cuboid(space, [0.2] * ndim, [0.8] * ndim)
grad = Gradient(space,
method=method,
pad_mode=pad_mode,
pad_const=pad_const)
grad(dom_vec)
def test_gradient(method, impl, padding):
"""Discretized spatial gradient operator."""
with pytest.raises(TypeError):
Gradient(odl.Rn(1), method=method)
if isinstance(padding, tuple):
padding_method, padding_value = padding
else:
padding_method, padding_value = padding, None
# DiscreteLp Vector
discr_space = odl.uniform_discr([0, 0], [1, 1], DATA_2D.shape, impl=impl)
dom_vec = discr_space.element(DATA_2D)
# computation of gradient components with helper function
dx0, dx1 = discr_space.cell_sides
diff_0 = finite_diff(DATA_2D, axis=0, dx=dx0, method=method,
padding_method=padding_method,
padding_value=padding_value)
diff_1 = finite_diff(DATA_2D, axis=1, dx=dx1, method=method,
padding_method=padding_method,
padding_value=padding_value)
# gradient
grad = Gradient(discr_space, method=method,
padding_method=padding_method,
padding_value=padding_value)
grad_vec = grad(dom_vec)
assert len(grad_vec) == DATA_2D.ndim
assert all_almost_equal(grad_vec[0].asarray(), diff_0)
assert all_almost_equal(grad_vec[1].asarray(), diff_1)
# Test adjoint operator
derivative = grad.derivative()
ran_vec = derivative.range.element([DATA_2D, DATA_2D ** 2])
deriv_grad_vec = derivative(dom_vec)
adj_grad_vec = derivative.adjoint(ran_vec)
lhs = ran_vec.inner(deriv_grad_vec)
rhs = dom_vec.inner(adj_grad_vec)
# Check not to use trivial data
assert lhs != 0
assert rhs != 0
assert almost_equal(lhs, rhs)
# higher dimensional arrays
lin_size = 3
for ndim in [1, 3, 6]:
# DiscreteLp Vector
space = odl.uniform_discr([0.] * ndim, [1.] * ndim, [lin_size] * ndim)
dom_vec = odl.phantom.cuboid(space, [0.2] * ndim, [0.8] * ndim)
# gradient
grad = Gradient(space, method=method,
padding_method=padding_method,
padding_value=padding_value)
grad(dom_vec)
def test_gradient(space, method, padding):
"""Discretized spatial gradient operator."""
places = 2 if space.dtype == np.float32 else 4
with pytest.raises(TypeError):
Gradient(odl.rn(1), method=method)
if isinstance(padding, tuple):
pad_mode, pad_const = padding
else:
pad_mode, pad_const = padding, 0
# DiscreteLp Vector
dom_vec = noise_element(space)
dom_vec_arr = dom_vec.asarray()
# gradient
grad = Gradient(space, method=method,
pad_mode=pad_mode,
pad_const=pad_const)
grad_vec = grad(dom_vec)
assert len(grad_vec) == space.ndim
# computation of gradient components with helper function
for axis, dx in enumerate(space.cell_sides):
diff = finite_diff(dom_vec_arr, axis=axis, dx=dx, method=method,
pad_mode=pad_mode,
pad_const=pad_const)
assert all_almost_equal(grad_vec[axis].asarray(), diff)
# Test adjoint operator
derivative = grad.derivative()
ran_vec = noise_element(derivative.range)
deriv_grad_vec = derivative(dom_vec)
adj_grad_vec = derivative.adjoint(ran_vec)
lhs = ran_vec.inner(deriv_grad_vec)
rhs = dom_vec.inner(adj_grad_vec)
# Check not to use trivial data
assert lhs != 0
assert rhs != 0
assert almost_equal(lhs, rhs, places=places)
# higher dimensional arrays
lin_size = 3
for ndim in [1, 3, 6]:
# DiscreteLpElement
space = odl.uniform_discr([0.] * ndim, [1.] * ndim, [lin_size] * ndim)
dom_vec = odl.phantom.cuboid(space, [0.2] * ndim, [0.8] * ndim)
# gradient
grad = Gradient(space, method=method,
pad_mode=pad_mode,
pad_const=pad_const)
grad(dom_vec)