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 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)))
from xii.meshing.embedded_mesh import EmbeddedMesh
import dolfin as df
import numpy as np
def is_close(a, b=0):
return abs(a - b) < 1E-12
mesh = df.UnitCubeMesh(4, 4, 8)
u = df.Function(df.FunctionSpace(mesh, 'CG', 2))
# Make 1d
f = df.MeshFunction('size_t', mesh, 1, 0)
df.CompiledSubDomain('near(x[0], 0.5) && near(x[1], 0.5)').mark(f, 1)
line_mesh = EmbeddedMesh(f, 1)
# Grow thicker with z
ci = Circle(radius=lambda x0: x0[2] / 2., degree=8)
reduced = Average(u, line_mesh, ci)
f = MeasureFunction(reduced)
# Circumnference of the circle
true = df.Expression('2*pi*x[2]/2', degree=1)
L = df.inner(f - true, f - true) * df.dx
assert is_close(df.sqrt(abs(df.assemble(L))))
di = Disk(radius=lambda x0: x0[2] / 2., degree=8)
reduced = Average(u, line_mesh, di)
f = MeasureFunction(reduced)
# Circumnference of the circle
true = df.Expression('pi*(x[2]/2)*(x[2]/2)', degree=2)
def mortar_meshes(subdomains,
markers,
ifacet_iter=None,
strict=True,
tol=1E-14):
'''
Let subdomains a cell function. We assume that domains (cells) marked
with the given markers are adjecent and an interface can be defined
between these domains which is a continuous curve. Then for each
domain we create a (sub)mesh and a single interface mesh which holds
a connectivity map of its cells to facets of the submeshes. The submeshes
are returned as a list. The connectivity map is
of the form submesh.id -> facets. The marking function f of the EmbeddedMesh
that is the interface is colored such that f[color] is the interface
of meshes (submeshes[m] for m color_map[color]).
'''
assert len(markers) > 1
# Need a cell function
mesh = subdomains.mesh()
tdim = mesh.topology().dim()
assert subdomains.dim() == tdim
markers = list(markers)
# For each facet we want to know which 2 cells share it
tagged_iface = defaultdict(dict)
if ifacet_iter is None:
mesh.init(tdim - 1)
ifacet_iter = df.facets(mesh)
mesh.init(tdim - 1, tdim)
for facet in ifacet_iter:
cells = map(int, facet.entities(tdim))
if len(cells) > 1:
c0, c1 = cells
tag0, tag1 = subdomains[c0], subdomains[c1]
if tag0 != tag1 and tag0 in markers and tag1 in markers:
# A key of sorted tags
if tag0 < tag1:
key = (tag0, tag1)
# The cells connected to facet order to match to tags
value = (c0, c1)
else:
key = (tag1, tag0)
value = (c1, c0)
# A facet to 2 cells map for the facets of tagged pair
tagged_iface[key][facet.index()] = value
# order -> tagged keys
color_to_tag_map = tagged_iface.keys()
# Set to color which won't be encounred
interface = df.MeshFunction('size_t', mesh, tdim - 1,
len(color_to_tag_map))
values = interface.array()
# Mark facets corresponding to tagged pair by a color
for color, tags in enumerate(color_to_tag_map):
values[tagged_iface[tags].keys()] = color
# Finally create an interface mesh for all the colors
interface_mesh = EmbeddedMesh(interface, range(len(color_to_tag_map)))
# Try to recogninze the meshes which violates assumptions by counting
assert not strict or is_continuous(interface_mesh)
# And subdomain mesh for each marker
subdomain_meshes = {tag: EmbeddedMesh(subdomains, tag) for tag in markers}
# Alloc the entity maps for the embedded mesh
interface_map = {
subdomain_meshes[tag].id(): [None] * interface_mesh.num_cells()
for tag in markers
}
# THe maps are filled by the following idea. Using marking function
# of interface mesh one cat get cells of that color and useing entity
# map for (original) mesh map the cells to mesh facet. A color also
# corresponds to a pair of tags which identifies the two meshes which
# share the facet - facet connected to 2 cells one for each mesh. The
# final step is to lean to map submesh cells to mesh cells
# local submesh <- global of parent mesh
sub_mesh_map = lambda tag: dict(
(mesh_c, submesh_c) for submesh_c, mesh_c in enumerate(
subdomain_meshes[tag].parent_entity_map[mesh.id()][tdim]))
# Thec cell-cell connectivity of each submesh
c2c = {tag: sub_mesh_map(tag) for tag in markers}
# A connectivity of interface mesh cells to facets of global mesh
c2f = interface_mesh.parent_entity_map[mesh.id()][tdim - 1]
for color, tags in enumerate(color_to_tag_map):
# Precompute for the 2 tags
submeshes = [subdomain_meshes[tag] for tag in tags]
for cell in df.SubsetIterator(interface_mesh.marking_function, color):
cell_index = cell.index()
# The corresponding global cell facet
facet = c2f[cell_index]
# The two cells in global mesh numbering
global_cells = tagged_iface[tags][facet]
# Let's find the facet in submesh
for tag, gc, submesh in zip(tags, global_cells, submeshes):
# The map uses local cell
local_cell = c2c[tag][gc]
mesh_id = submesh.id()
found = False
for submesh_facet in df.facets(df.Cell(submesh, local_cell)):
found = df.near(
cell.midpoint().distance(submesh_facet.midpoint()), 0,
tol)
if found:
interface_map[mesh_id][
cell_index] = submesh_facet.index()
break
# Collapse to list; I want list indexing
subdomain_meshes = np.array([subdomain_meshes[m] for m in markers])
color_map = [map(markers.index, tags) for tags in color_to_tag_map]
# Parent in the sense that the colored piece of interface
# could have been created from mesh
interface_mesh.parent_entity_map.update(
dict((k, {
tdim - 1: v
}) for k, v in interface_map.items()))
return subdomain_meshes, interface_mesh, color_map