def identity_test(n, shape, subd=I_curve):
'''Averaging over indep coords of f'''
true = df.Expression('x[2]*x[2]', degree=2)
mesh = df.UnitCubeMesh(n, n, n)
V = df.FunctionSpace(mesh, 'CG', 2)
v = df.interpolate(true, V)
f = df.MeshFunction('size_t', mesh, 1, 0)
subd.mark(f, 1)
line_mesh = EmbeddedMesh(f, 1)
Q = average_space(V, line_mesh)
q = df.Function(Q)
Pi = avg_mat(V, Q, line_mesh, {'shape': shape})
Pi.mult(v.vector(), q.vector())
q0 = true
# Error
L = df.inner(q0 - q, q0 - q) * df.dx
e = q.vector().copy()
e.axpy(-1, df.interpolate(q0, Q).vector())
return df.sqrt(abs(df.assemble(L)))
def square_test(n, subd=I_curve):
'''Averaging over indep coords of f'''
size = 0.1
shape = Square(P=lambda x0: x0 - np.array(
[size + size * x0[2], size + size * x0[2], 0]),
degree=10)
foo = df.Expression('x[2]*((x[0]-0.5)*(x[0]-0.5) + (x[1]-0.5)*(x[1]-0.5))',
degree=3)
mesh = df.UnitCubeMesh(n, n, n)
V = df.FunctionSpace(mesh, 'CG', 3)
v = df.interpolate(foo, V)
f = df.MeshFunction('size_t', mesh, 1, 0)
subd.mark(f, 1)
true = df.Expression('x[2]*2./3*(size+size*x[2])*(size+size*x[2])',
degree=4,
size=size)
line_mesh = EmbeddedMesh(f, 1)
Q = average_space(V, line_mesh)
q = df.Function(Q)
Pi = avg_mat(V, Q, line_mesh, {'shape': shape})
Pi.mult(v.vector(), q.vector())
q0 = true
# Error
L = df.inner(q0 - q, q0 - q) * df.dx
e = q.vector().copy()
e.axpy(-1, df.interpolate(q0, Q).vector())
return df.sqrt(abs(df.assemble(L)))
def disk_test(n, subd=I_curve):
'''Averaging over indep coords of f'''
shape = Disk(radius=lambda x0: 0.1 + 0.0 * x0[2] / 2, degree=10)
foo = df.Expression('x[2]*((x[0]-0.5)*(x[0]-0.5) + (x[1]-0.5)*(x[1]-0.5))',
degree=3)
mesh = df.UnitCubeMesh(n, n, n)
V = df.FunctionSpace(mesh, 'CG', 3)
v = df.interpolate(foo, V)
f = df.MeshFunction('size_t', mesh, 1, 0)
subd.mark(f, 1)
true = df.Expression('x[2]*(0.1+0.0*x[2]/2)*(0.1+0.0*x[2]/2)/2', degree=4)
line_mesh = EmbeddedMesh(f, 1)
Q = average_space(V, line_mesh)
q = df.Function(Q)
Pi = avg_mat(V, Q, line_mesh, {'shape': shape})
Pi.mult(v.vector(), q.vector())
q0 = true
# Error
L = df.inner(q0 - q, q0 - q) * df.dx
e = q.vector().copy()
e.axpy(-1, df.interpolate(q0, Q).vector())
return df.sqrt(abs(df.assemble(L)))
def MeasureFunction(averaged):
'''Get measure of the averaging shape as a function on the 1d surface'''
# Get space on 1d mesh for the measure
V = averaged.function_space() # 3d one
# Want the measure in scalar space
if V.ufl_element().value_shape(): V = V.sub(0).collapse()
mesh_1d = averaged.average_['mesh']
# Finally
TV = average_space(V, mesh_1d)
TV_coordinates = TV.tabulate_dof_coordinates().reshape((TV.dim(), -1))
TV_dm = TV.dofmap()
visited = np.zeros(TV.dim(), dtype=bool)
mesh_x = mesh_1d.coordinates()
shape = averaged.average_['shape']
values = np.empty(TV.dim(), dtype=float)
for cell in cells(mesh_1d):
# Get the tangent (normal of the plane which cuts the virtual
# surface to yield the bdry curve
v0, v1 = mesh_x[cell.entities(0)]
n = v0 - v1
# Where to
dofs = TV_dm.cell_dofs(cell.index())
for seen, dof in zip(visited[dofs], dofs):
if not seen:
x = TV_coordinates[dof]
# Values sum up to the measure of the hypersurface
values[dof] = sum(shape.quadrature(x, n).weights)
visited[dof] = True
assert np.all(visited)
# Wrap as a function
m = Function(TV)
m.vector().set_local(values)
return m
def render_avg_surface(Pi):
'''Plot the averaging surface via looking at the quadrature points used'''
V = Pi.function_space()
line_mesh = Pi.average_['mesh']
shape = Pi.average_['shape']
# Where the average will be represented
Pi_V = average_space(V, line_mesh)
# We produce a curve of quardrature points for each dof
surface = []
dm = Pi_V.dofmap()
dofs_x = Pi_V.tabulate_dof_coordinates().reshape((Pi_V.dim(), -1))
for cell in df.cells(line_mesh):
v0, v1 = cell.get_vertex_coordinates().reshape((2, 3))
n = v1 - v0
for dof_x in dofs_x[dm.cell_dofs(cell.index())]:
x = np.row_stack(shape.quadrature(dof_x, n).points)
surface.append(x)
return surface
def sanity_test(n, subd, shape):
'''Constant is preserved'''
mesh = df.UnitCubeMesh(n, n, n)
V = df.FunctionSpace(mesh, 'CG', 2)
v = df.interpolate(df.Constant(1), V)
f = df.MeshFunction('size_t', mesh, 1, 0)
subd.mark(f, 1)
line_mesh = EmbeddedMesh(f, 1)
Q = average_space(V, line_mesh)
q = df.Function(Q)
Pi = avg_mat(V, Q, line_mesh, {'shape': shape})
Pi.mult(v.vector(), q.vector())
q0 = df.Constant(1)
# Error
L = df.inner(q0 - q, q0 - q) * df.dx
e = q.vector().copy()
e.axpy(-1, df.interpolate(q0, Q).vector())
return df.sqrt(abs(df.assemble(L)))
