def read(self, t, field_name=None):
with open(self.jsonfilename) as jsonfile:
fieldsDict = json.load(jsonfile, object_pairs_hook=OrderedDict)
jsonfile.close()
self.mesh = self.load_mesh()
if self.field_name is None:
self.field_name = field_name
self.fs_type = fieldsDict[self.field_name]['metadata']['type']
self.family = fieldsDict[self.field_name]['metadata']['family']
self.degree = fieldsDict[self.field_name]['metadata']['degree']
if self.fs_type == 'vector':
self.dim = fieldsDict[self.field_name]['metadata']['dim']
self.functionspace = df.VectorFunctionSpace(
self.mesh, self.family, self.degree, self.dim)
elif self.fs_type == 'scalar':
self.functionspace = df.FunctionSpace(self.mesh, str(self.family),
self.degree)
name = str([
item[0] for item in fieldsDict[self.field_name]['data'].items()
if abs(item[1] - t) < 1e-10
][0])
f_loaded = df.Function(self.functionspace)
self.h5file.read(f_loaded, name)
return f_loaded
def setUp(self):
np.random.seed(1)
#self.dim = np.random.randint(1, high=5)
self.dim = 1
self.means = np.random.uniform(-10, high=10., size=self.dim)
self.chol = np.tril(
np.random.uniform(1, high=10, size=(self.dim, self.dim)))
self.cov = np.dot(self.chol, self.chol.T)
self.precision = np.linalg.inv(self.cov)
mesh = dl.RectangleMesh(dl.mpi_comm_world(), dl.Point(0.0, 0.0),
dl.Point(3, 2), 6, 4)
if self.dim > 1:
self.Rn = dl.VectorFunctionSpace(mesh, "R", 0, dim=self.dim)
else:
self.Rn = dl.FunctionSpace(mesh, "R", 0)
self.test_prior = GaussianRealPrior(self.Rn, self.cov)
m = dl.Function(self.Rn)
m.vector().zero()
m.vector().set_local(self.means)
self.test_prior.mean.axpy(1., m.vector())
def load_local_basis(h5file, lgroup, mesh, geo):
if h5file.has_dataset(lgroup):
# Get local bais functions
local_basis_attrs = h5file.attributes(lgroup)
lspace = local_basis_attrs["space"]
family, order = lspace.split("_")
namesstr = local_basis_attrs["names"]
names = namesstr.split(":")
if DOLFIN_VERSION_MAJOR > 1.6:
elm = dolfin.VectorElement(
family=family,
cell=mesh.ufl_cell(),
degree=int(order),
quad_scheme="default",
)
V = dolfin.FunctionSpace(mesh, elm)
else:
V = dolfin.VectorFunctionSpace(mesh, family, int(order))
for name in names:
lb = Function(V, name=name)
io_utils.read_h5file(h5file, lb, lgroup + "/{}".format(name))
setattr(geo, name, lb)
else:
setattr(geo, "circumferential", None)
setattr(geo, "radial", None)
setattr(geo, "longitudinal", None)
def unitcube_geometry():
N = 3
mesh = UnitCubeMesh(mpi_comm_world(), N, N, N)
ffun = dolfin.MeshFunction("size_t", mesh, 2)
ffun.set_all(0)
fixed.mark(ffun, fixed_marker)
free.mark(ffun, free_marker)
marker_functions = MarkerFunctions(ffun=ffun)
# Fibers
V_f = dolfin.VectorFunctionSpace(mesh, "CG", 1)
f0 = interpolate(Expression(("1.0", "0.0", "0.0"), degree=1), V_f)
s0 = interpolate(Expression(("0.0", "1.0", "0.0"), degree=1), V_f)
n0 = interpolate(Expression(("0.0", "0.0", "1.0"), degree=1), V_f)
microstructure = Microstructure(f0=f0, s0=s0, n0=n0)
geometry = Geometry(
mesh=mesh,
marker_functions=marker_functions,
microstructure=microstructure,
)
return geometry
def get_demagfield(self, phi=None, use_default_function_space=True):
"""
Returns the projection of the negative gradient of
phi onto a DG0 space defined on the same mesh
Note: Do not trust the viper solver to plot the DeMag field,
it can give some wierd results, paraview is recommended instead
use_default_function_space - If true project into self.W,
if false project into a Vector DG0 space
over the mesh of phi.
"""
if phi is None:
phi = self.phi
Hdemag = -df.grad(phi)
if use_default_function_space == True:
Hdemag = df.project(Hdemag, self.W)
else:
if self.D == 1:
Hspace = df.FunctionSpace(phi.function_space().mesh(), "DG", 0)
else:
Hspace = df.VectorFunctionSpace(phi.function_space().mesh(),
"DG", 0)
Hdemag = df.project(Hdemag, Hspace)
return Hdemag
def efield_DG0(mesh, phi):
Q = df.VectorFunctionSpace(mesh, 'DG', 0)
q = df.TestFunction(Q)
M = (1.0 / df.CellVolume(mesh)) * df.inner(-df.grad(phi), q) * df.dx
E_dg0 = df.assemble(M)
return df.Function(Q, E_dg0)
def _space(mesh, rank):
if rank==0:
return dolfin.FunctionSpace(mesh, "CG", 1)
elif rank==1:
return dolfin.VectorFunctionSpace(mesh, "CG", 1)
else:
raise Exception("Loading Functions of rank > 1 is not supported.")
def set_spaces_functions(self, p):
# p: int, polynomial order of trial/test functions
nvar = self.params['nvar']
mesh = self.mesh
# Define finite-element spaces
U = d.FunctionSpace(mesh, "CG", p)
W = d.VectorFunctionSpace(mesh, "CG", p, dim = nvar-1)
# Functions for bilinear and linear form
self.ut = d.TrialFunction(U)
self.du = d.TestFunction(U)
self.un = d.Function(U)
self.u = d.Function(U)
self.w = d.Function(W)
# Auxiliar functions
self.sig = d.Function(U) # conductivity value
self.f = d.Function(U) # conductivity value
# Save Spaces
self.U = U
self.W = W
self.ndofs = U.tabulate_dof_coordinates().shape[0]
def test_anisotropy_energy_analytical(fixt):
"""
Compare one UniaxialAnisotropy energy with the corresponding analytical result.
The magnetisation is m = (0, sqrt(1 - x^2), x) and the easy axis still
a = (0, 0, 1). The squared dot product in the energy integral thus gives
dot(a, m)^2 = x^2. Integrating x^2 gives (x^3)/3 and the analytical
result with the constants we have chosen is 1 - 1/3 = 2/3.
"""
mesh = df.UnitCubeMesh(1, 1, 1)
functionspace = df.VectorFunctionSpace(mesh, "Lagrange", 1)
K1 = 1
Ms = Field(df.FunctionSpace(mesh, 'DG', 0), 1)
a = df.Constant((0, 0, 1))
m = Field(functionspace)
m.set(df.Expression(("0", "sqrt(1 - pow(x[0], 2))", "x[0]"), degree=1))
anis = UniaxialAnisotropy(K1, a)
anis.setup(m, Ms)
E = anis.compute_energy()
expected_E = float(2) / 3
print "With m = (0, sqrt(1-x^2), x), expecting E = {}. Got E = {}.".format(
expected_E, E)
#assert abs(E - expected_E) < TOLERANCE
assert np.allclose(E, expected_E, atol=1e-14, rtol=TOLERANCE)
def test_field2_has_more_timesteps_than_field1():
filename = 'file_usage_diff'
functionspace = df.VectorFunctionSpace(mesh, 'CG', 1, 3)
f1 = df.Function(functionspace)
f2 = df.Function(functionspace)
h5file = openh5(filename, functionspace, mode='w')
h5file.save_mesh()
for t in t_array:
f1.assign(df.Constant((t, 0, 0)))
f2.assign(df.Constant((t, 1, 0)))
h5file.write(f1, 'f1', t)
h5file.write(f2, 'f2', t)
f2.assign(df.Constant((50, 1, 0)))
h5file.write(f2, 'f2', t)
h5file = openh5(filename, mode='r')
print h5file.fields()
for field in h5file.fields():
for t in h5file.times(field):
h5file.read(t_array[1], field)
os.system('rm file_usage_diff.h5')
os.system('rm file_usage_diff.json')
def get_field(self):
"""
compute field
.. todo:: find better representation for real and imaginary part
.. todo:: decide whether field should be of same order as solution
"""
self.logger.debug("Entering Field computation")
if self.data['degree'] > 1:
self.Vector = d.VectorFunctionSpace(
self.mesh.mesh, self.data['properties']['project_element'],
self.data['properties']['project_degree'])
else:
raise Exception(
"Use for this computation a 2nd or higher order element")
self.Efield_real = d.project(-d.grad(self.u.sub(0)),
self.Vector,
solver_type='mumps')
self.Efield_imag = d.project(-d.grad(self.u.sub(1)),
self.Vector,
solver_type='mumps')
self.Efield_real.rename('E-field real', 'E-field real')
self.Efield_imag.rename('E-field imag', 'E-field imag')
self.logger.debug("Computed E-Field")
def test_volume(volumes):
geo = pulse.HeartGeometry.from_file(pulse.mesh_paths["simple_ellipsoid"])
V_cg2 = dolfin.VectorFunctionSpace(geo.mesh, "CG", 2)
u = dolfin_adjoint.Function(V_cg2)
volume_obs = VolumeObservation(mesh=geo.mesh,
dmu=geo.ds(geo.markers["ENDO"]),
description="Test LV volume")
model_volume = volume_obs(u).vector().get_local()[0]
target = OptimizationTarget(volumes, volume_obs, collect=True)
if np.isscalar(volumes):
volumes = (volumes, )
fs = []
for t, v in zip(target, volumes):
fun = dolfin.project(t.assign(u), t._V)
f = fun.vector().get_local()[0]
fs.append(f)
g = (model_volume - v)**2
assert abs(f - g) < 1e-12
# We collect the data
assert np.all(
np.subtract(
np.squeeze([v.get_local()
for v in target.collector["data"]]), volumes) < 1e-12)
assert np.all(
np.subtract(
np.squeeze([v.get_local() for v in target.collector["model"]]),
model_volume) < 1e-12)
assert np.all(np.subtract(target.collector["functional"], fs) < 1e-12)
def main():
problem, control = fixtures.create_problem()
geo = problem.geometry
V_cg2 = dolfin.VectorFunctionSpace(geo.mesh, "CG", 2)
u = dolfin.Function(V_cg2)
volume_obs = VolumeObservation(mesh=geo.mesh,
dmu=geo.ds(geo.markers["ENDO"]),
description="Test LV volume")
model_volume = volume_obs(u).vector().get_local()[0]
print(model_volume)
volumes = (2.7, 2.9)
# volumes = (2.7,)
target = OptimizationTarget(volumes, volume_obs, collect=True)
lvp = [0.5, 1.0]
# lvp = [0.5]
bcs = BoundaryObservation(bc=problem.bcs.neumann[0], data=lvp)
assimilator = Assimilator(problem, target, bcs, control)
assimilator.assimilate()
from IPython import embed
embed()
exit()
def test_crossprod():
"""
Compute the cross product of two functions f and g numerically
using `helpers.crossprod` and compare with the analytical
expression.
"""
xmin = ymin = zmin = -2
xmax = ymax = zmax = 3
nx = ny = nz = 10
mesh = df.BoxMesh(df.Point(xmin, ymin, zmin), df.Point(xmax, ymax, zmax),
nx, ny, nz)
V = df.VectorFunctionSpace(mesh, 'CG', 1, dim=3)
u = df.interpolate(df.Expression(['x[0]', 'x[1]', '0'], degree=1), V)
v = df.interpolate(df.Expression(['-x[1]', 'x[0]', 'x[2]'], degree=1), V)
w = df.interpolate(
df.Expression(['x[1]*x[2]', '-x[0]*x[2]', 'x[0]*x[0]+x[1]*x[1]'],
degree=1), V)
a = u.vector().array()
b = v.vector().array()
c = w.vector().array()
axb = crossprod(a, b)
assert (np.allclose(axb, c))
def convert3D(mesh3D, *forces):
"convert force from axisymmetric 2D simulation to 3D vector function"
def rad(x, y):
return dolfin.sqrt(x**2 + y**2)
class Convert3DExpression(dolfin.Expression):
def __init__(self, F, **kwargs):
self.F = F
def value_shape(self):
return (3,)
def eval(self, value, x):
r = rad(x[0], x[1])
F = self.F([r, x[2]])
if r==0.:
value[0] = 0.
value[1] = 0.
value[2] = F[1]
else:
value[0] = x[0]/r*F[0]
value[1] = x[1]/r*F[0]
value[2] = F[1]
#U = dolfin.FunctionSpace(mesh3D, "CG", 1)
#V = dolfin.MixedFunctionSpace([U, U, U])
V = dolfin.VectorFunctionSpace(mesh3D, "CG", 1)
def to3D(F):
F2 = dolfin.project(Convert3DExpression(F, degree=1), V)
#F2 = dolfin.Function(V)
#F2.interpolate(Convert3DExpression(F))
return F2
return tuple(map(to3D, forces))
def test_exchange_field_supported_methods(fixt):
"""
Check that all supported methods give the same results
as the default method.
"""
A = 1
REL_TOLERANCE = 1e-12
mesh = df.UnitCubeMesh(10, 10, 10)
Ms = Field(df.FunctionSpace(mesh, 'DG', 0), 1)
functionspace = df.VectorFunctionSpace(mesh, "CG", 1, 3)
m = Field(functionspace)
m.set(df.Expression(("0", "sin(x[0])", "cos(x[0])"), degree=1))
exch = Exchange(A)
exch.setup(m, Ms)
H_default = exch.compute_field()
supported_methods = list(Exchange._supported_methods)
# no need to compare default method with itself
supported_methods.remove(exch.method)
# the project method for the exchange is too bad
supported_methods.remove("project")
for method in supported_methods:
exch = Exchange(A, method=method)
exch.setup(m, Ms)
H = exch.compute_field()
print "With method '{}', expecting H =\n{}\n, got H =\n{}.".format(
method,
H_default.reshape((3, -1)).mean(1),
H.reshape((3, -1)).mean(1))
rel_diff = np.abs((H - H_default) / H_default)
assert np.nanmax(rel_diff) < REL_TOLERANCE
def diffusivity_field_simple(setup, r, ddata_bulk, boundary="poresolidb"):
"interpolates diffusivity field defined on the geometry given by setup"
# build 1D interpolations from data
fn, ft = preprocess_Dr(ddata_bulk, r)
setup.prerefine(visualize=True)
dist = distance_boundary_from_geo(setup.geo, boundary)
VV = dolfin.VectorFunctionSpace(setup.geo.mesh, "CG", 1)
normal = dolfin.project(dolfin.grad(dist), VV)
phys = setup.phys
D0 = phys.kT / (6. * np.pi * phys.eta * r * 1e-9)
def DBulk(x, i):
r = dist(x)
n = normal(x)
Dn = D0 * float(fn(r))
Dt = D0 * float(ft(r))
D = transformation(n, Dn, Dt)
return D[i][i]
D = lambda i: dict(fluid=lambda x: DBulk(x, i), )
dim = setup.geop.dim
DD = [harmonic_interpolation(setup, subdomains=D(i)) for i in range(dim)]
D = dolfin.Function(VV)
dolfin.assign(D, DD)
return dict(dist=dist, D=D)
def test_exchange_energy_analytical_2():
"""
Compare one Exchange energy with the corresponding analytical result.
"""
REL_TOLERANCE = 5e-5
lx = 6
ly = 3
lz = 2
nx = 300
ny = nz = 1
mesh = df.BoxMesh(df.Point(0, 0, 0), df.Point(lx, ly, lz), nx, ny, nz)
unit_length = 1e-9
functionspace = df.VectorFunctionSpace(mesh, "CG", 1, 3)
Ms = Ms = Field(df.FunctionSpace(mesh, 'DG', 0), 8e5)
A = 13e-12
m = Field(functionspace)
m.set(
df.Expression(['0', 'sin(2*pi*x[0]/l_x)', 'cos(2*pi*x[0]/l_x)'],
l_x=lx,
degree=1))
exch = Exchange(A)
exch.setup(m, Ms, unit_length=unit_length)
E_expected = A * 4 * pi ** 2 * \
(ly * unit_length) * (lz * unit_length) / (lx * unit_length)
E = exch.compute_energy()
print "expected energy: {}".format(E)
print "computed energy: {}".format(E_expected)
assert abs((E - E_expected) / E_expected) < REL_TOLERANCE
def __init__(self, mesh, orientation):
# Manifold assumption
assert 1 <= mesh.topology().dim() < mesh.geometry().dim()
gdim = mesh.geometry().dim()
# Orientation from inside point
if isinstance(orientation, (list, np.ndarray, tuple)):
assert len(orientation) == gdim
kwargs = {'x0%d' % i: val for i, val in enumerate(orientation)}
orientation = ['x[%d] - x0%d' % (i, i) for i in range(gdim)]
orientation = df.Expression(orientation, degree=1, **kwargs)
assert orientation.ufl_shape == (gdim, )
V = df.VectorFunctionSpace(mesh, 'DG', 0, gdim)
df.Function.__init__(self, V)
n_values = self.vector().get_local()
values = []
for cell in df.cells(mesh):
n = cell.cell_normal().array()[:gdim]
x = cell.midpoint().array()[:gdim]
# Disagree?
if np.inner(orientation(x), n) < 0:
n *= -1
values.append(n / np.linalg.norm(n))
values = np.array(values)
for sub in range(gdim):
dofs = V.sub(sub).dofmap().dofs()
n_values[dofs] = values[:, sub]
self.vector().set_local(n_values)
self.vector().apply('insert')
def generate_function_spaces(self, order=1, use_periodic=False):
r"""
Generates the finite-element function spaces used with topological dimension :math:`d` set by variable ``self.top_dim``.
:param order: order :math:`k` of the shape function, currently only supported are Lagrange :math:`P_1` elements.
:param use_periodic: use periodic boundaries along lateral boundary (currently not supported).
:type use_periodic: bool
The element shape-functions available from this method are :
* ``self.Q`` -- :math:`\mathcal{H}^k(\Omega)`
* ``self.Q3`` -- :math:`[\mathcal{H}^k(\Omega)]^3`
* ``self.V`` -- :math:`[\mathcal{H}^k(\Omega)]^d` formed using :func:`~dolfin.functions.functionspace.VectorFunctionSpace`
* ``self.T`` -- :math:`[\mathcal{H}^k(\Omega)]^{d \times d}` formed using :func:`~dolfin.functions.functionspace.TensorFunctionSpace`
"""
s = "::: generating fundamental function spaces of order %i :::" % order
print_text(s, cls=self.this)
if use_periodic:
self.generate_pbc()
else:
self.pBC = None
order = 1
space = 'CG'
Qe = dl.FiniteElement("CG", self.mesh.ufl_cell(), order)
self.Q3 = dl.FunctionSpace(self.mesh, dl.MixedElement([Qe] * 3))
self.Q = dl.FunctionSpace(self.mesh, space, order)
self.V = dl.VectorFunctionSpace(self.mesh, space, order)
self.T = dl.TensorFunctionSpace(self.mesh, space, order)
s = " - fundamental function spaces created - "
print_text(s, cls=self.this)
def setup():
"""
Create a cuboid mesh representing a magnetic material and two
dolfin.Functions defined on this mesh:
m -- unit magnetisation (linearly varying across the sample)
Ms_func -- constant function representing the saturation
magnetisation Ms
*Returns*
A triple (m_space, m, Ms_func), where m_space is the
VectorFunctionSpace (of type "continuous Lagrange") on which the
magnetisation m is defined and m, Ms_funct are as above.
"""
m_space = df.VectorFunctionSpace(mesh, "CG", 1)
m = Field(m_space, value=df.Expression(("1e-9", "x[0]/10", "0"), degree=1))
m.set_with_numpy_array_debug(fnormalise(m.get_numpy_array_debug()))
Ms_space = df.FunctionSpace(mesh, "DG", 0)
Ms_func = df.interpolate(df.Constant(Ms), Ms_space)
return m_space, m, Ms_func
def load_vector_field_hdf5(hdf, name, mesh, pd):
"""
Load a vector-valued function from an HDF5 file.
Parameters
----------
hdf : str, dolfin.HDF5File
The name of the file where the cell functions are stored, or the
handle to the an HDF5 file.
name : str
The name under which the vector-valued function is stored in the HDF5 file.
mesh : dolfin.Mesh
The computational mesh over which the fiber directions need to be
defined. This is needed to create the corresponding mesh functions.
pd : int
The polynomial degree used to approximate the vector field describing
the fiber directions.
Returns
-------
vector_field : dolfin.Function
The vector-valued dolfin.Function object describing the fiber directions.
"""
family = "DG" if pd == 0 else "CG"
V = dlf.VectorFunctionSpace(mesh, family, pd)
vector_field = dlf.Function(V)
hdf.read(vector_field, name)
return vector_field
def convert2D(mesh2D, *forces):
"convert force from axisymmetric 2D simulation to 2D vector function"
dolfin.parameters['allow_extrapolation'] = False
class Convert2DExpression(dolfin.Expression):
def __init__(self, F, **kwargs):
self.F = F
def value_shape(self):
return (2, )
def eval(self, value, x):
r = abs(x[0])
F = self.F([r, x[1]])
if r == 0.:
value[0] = 0.
value[1] = F[1]
else:
value[0] = x[0] / r * F[0]
value[1] = F[1]
#U = dolfin.FunctionSpace(mesh2D, "CG", 1)
#V = dolfin.MixedFunctionSpace([U, U])
V = dolfin.VectorFunctionSpace(mesh2D, "CG", 1)
def to2D(F):
F2 = dolfin.project(Convert2DExpression(F, degree=1), V)
#F2 = dolfin.Function(V)
#F2.interpolate(Convert3DExpression(F))
return F2
return tuple(map(to2D, forces))
def convert_meshfunctions_to_vectorfunction(meshfunctions, functionspace=None):
"""
Convert a list of mesh functions to a vector-valued dolfin.Function object.
Parameters
----------
meshfunctions : list, tuple
The list/tuple of mesh function objects used to create a vector-valued
function. These are first converted to scalar-valued functions, and then
they are assigned to the components of the vector-valued function.
functionspace : dolfin.FunctionSpace (default None)
The function space used to create the dolfin.Function object. A new
function space with element 'p0' is created if None is provided.
Returns
-------
vector_func : dolfin.Function
The vector-valued dolfin.Function object created to describe the fiber
directions specified by the list of mesh functions.
"""
func_components = list()
for i, mf in enumerate(meshfunctions):
func_components.append(convert_meshfunction_to_function(mf))
mesh = meshfunctions[0].mesh()
if functionspace is None:
functionspace = dlf.VectorFunctionSpace(mesh, "DG", 0)
vector_func = dlf.Function(functionspace)
assign_scalars_to_vectorfunctions(func_components, vector_func)
return vector_func
def __init__(self, mesh, Ms=8e5, unit_length=1.0, name='unnamed', auto_save_data=True):
self.mesh = mesh
self.unit_length=unit_length
self.DG = df.FunctionSpace(mesh, "DG", 0)
self.DG3 = df.VectorFunctionSpace(mesh, "DG", 0, dim=3)
self._m = df.Function(self.DG3)
self.Ms = Ms
self.nxyz_cell=mesh.num_cells()
self._alpha = np.zeros(self.nxyz_cell)
self.m = np.zeros(3*self.nxyz_cell)
self.H_eff = np.zeros(3*self.nxyz_cell)
self.dm_dt = np.zeros(3*self.nxyz_cell)
self.set_default_values()
self.auto_save_data = auto_save_data
self.sanitized_name = helpers.clean_filename(name)
if self.auto_save_data:
self.ndtfilename = self.sanitized_name + '.ndt'
self.tablewriter = Tablewriter(self.ndtfilename, self, override=True)
def setup(self):
"""
Create mesh velocity and deformation functions
"""
sim = self.simulation
assert self.active is False, 'Trying to setup mesh morphing twice in the same simulation'
# Store previous cell volumes
mesh = sim.data['mesh']
Vcvol = dolfin.FunctionSpace(mesh, 'DG', 0)
sim.data['cvolp'] = dolfin.Function(Vcvol)
# The function spaces for mesh velocities and displacements
Vmesh = dolfin.FunctionSpace(mesh, 'CG', 1)
Vmesh_vec = dolfin.VectorFunctionSpace(mesh, 'CG', 1)
sim.data['Vmesh'] = Vmesh
# Create mesh velocity functions
u_mesh = []
for d in range(sim.ndim):
umi = dolfin.Function(Vmesh)
sim.data['u_mesh%d' % d] = umi
u_mesh.append(umi)
u_mesh = dolfin.as_vector(u_mesh)
sim.data['u_mesh'] = u_mesh
# Create mesh displacement vector function
self.displacement = dolfin.Function(Vmesh_vec)
self.assigners = [
dolfin.FunctionAssigner(Vmesh_vec.sub(d), Vmesh)
for d in range(sim.ndim)
]
self.active = True
def make_crl_basis(mesh, foc):
"""
Makes the crl basis for the LV mesh (prolate ellipsoidal)
with prespecified focal length.
"""
VV = dolfin.VectorFunctionSpace(mesh, "CG", 1)
V = dolfin.FunctionSpace(mesh, "CG", 1)
if DOLFIN_VERSION_MAJOR > 1.6:
dofs_x = V.tabulate_dof_coordinates().reshape(
(-1, mesh.geometry().dim()))
else:
dm = V.dofmap()
dofs_x = dm.tabulate_all_coordinates(mesh).reshape(
(-1, mesh.geometry().dim()))
e_c = make_unit_vector(V, VV, dofs_x, fill_coordinates_ec)
e_l = make_unit_vector(V, VV, dofs_x, fill_coordinates_el, foc)
e_r = calc_cross_products(e_c, e_l, VV)
e_c.rename("c0", "local_basis_function")
e_r.rename("r0", "local_basis_function")
e_l.rename("l0", "local_basis_function")
return e_c, e_r, e_l
def test_demag_2d(plot=False):
mesh = df.UnitSquareMesh(4, 4)
Ms = 1.0
S3 = df.VectorFunctionSpace(mesh, "Lagrange", 1, dim=3)
m0 = df.Expression(("0", "0", "1"), degree=1)
m = Field(S3, m0)
h = 0.001
demag = Demag2D(thickness=h)
demag.setup(m, Ms)
print demag.compute_field()
f0 = demag.compute_field()
m.set_with_numpy_array_debug(f0)
print demag.m.probe(0., 0., 0)
print demag.m.probe(1., 0., 0)
print demag.m.probe(0., 1., 0)
print demag.m.probe(1., 1., 0)
print '=' * 50
print demag.m.probe(0., 0., h)
print demag.m.probe(1., 0., h)
print demag.m.probe(0., 1., h)
print demag.m.probe(1., 1., h)
if plot:
df.plot(m.f)
df.interactive()
def computeVelocityField(mesh):
Xh = dl.VectorFunctionSpace(mesh, 'Lagrange', 2)
Wh = dl.FunctionSpace(mesh, 'Lagrange', 1)
XW = dl.MixedFunctionSpace([Xh, Wh])
Re = 1e2
g = dl.Expression(('0.0', '(x[0] < 1e-14) - (x[0] > 1 - 1e-14)'))
bc1 = dl.DirichletBC(XW.sub(0), g, v_boundary)
bc2 = dl.DirichletBC(XW.sub(1), dl.Constant(0), q_boundary, 'pointwise')
bcs = [bc1, bc2]
vq = dl.Function(XW)
(v, q) = dl.split(vq)
(v_test, q_test) = dl.TestFunctions(XW)
def strain(v):
return dl.sym(dl.nabla_grad(v))
F = ((2. / Re) * dl.inner(strain(v), strain(v_test)) +
dl.inner(dl.nabla_grad(v) * v, v_test) - (q * dl.div(v_test)) +
(dl.div(v) * q_test)) * dl.dx
dl.solve(F == 0,
vq,
bcs,
solver_parameters={
"newton_solver": {
"relative_tolerance": 1e-4,
"maximum_iterations": 100
}
})
return v
def __init__(self, mesh):
assert 1 <= mesh.topology().dim() < mesh.geometry().dim()
gdim = mesh.geometry().dim()
V = df.VectorFunctionSpace(mesh, 'DG', 0, gdim)
df.Function.__init__(self, V)
n_values = self.vector().get_local()
X = mesh.coordinates()
c2v = mesh.topology()(1, 0)
_, v2c = mesh.init(0, 1), mesh.topology()(0, 1)
cell_f, bcolors, lcolors = color_branches(mesh)
loop_flags = chain(repeat(False, len(bcolors)),
repeat(True, len(lcolors)))
colors = chain(bcolors, lcolors)
cell_f = cell_f.array()
values = np.zeros(V.dim()).reshape((-1, gdim))
for color, lf in zip(colors, loop_flags):
for cell, orient in walk_cells(cell_f,
tag=color,
c2v=c2v,
v2c=v2c,
is_loop=lf):
v0, v1 = X[c2v(cell) if orient else c2v(cell)[::-1]]
t = (v1 - v0) / np.linalg.norm(v1 - v0)
values[cell][:] = t
for sub in range(gdim):
dofs = V.sub(sub).dofmap().dofs()
n_values[dofs] = values[:, sub]
self.vector().set_local(n_values)
self.vector().apply('insert')