Exemplo n.º 1
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    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
Exemplo n.º 2
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    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())
Exemplo n.º 3
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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)
Exemplo n.º 4
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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
Exemplo n.º 5
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    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
Exemplo n.º 6
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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)
Exemplo n.º 7
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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.")
Exemplo n.º 8
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 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]
Exemplo n.º 9
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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)
Exemplo n.º 10
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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')
Exemplo n.º 11
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    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)
Exemplo n.º 13
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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()
Exemplo n.º 14
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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))
Exemplo n.º 15
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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))
Exemplo n.º 16
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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
Exemplo n.º 17
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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)
Exemplo n.º 18
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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
Exemplo n.º 19
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    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')
Exemplo n.º 20
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    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
Exemplo n.º 22
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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
Exemplo n.º 23
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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))
Exemplo n.º 24
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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
Exemplo n.º 25
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 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)
Exemplo n.º 26
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    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
Exemplo n.º 27
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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
Exemplo n.º 28
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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()
Exemplo n.º 29
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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
Exemplo n.º 30
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    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')