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function.py
620 lines (493 loc) · 22.2 KB
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function.py
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# Copyright (C) 2009-2022 Chris N. Richardson, Garth N. Wells and Michal Habera
#
# This file is part of DOLFINx (https://www.fenicsproject.org)
#
# SPDX-License-Identifier: LGPL-3.0-or-later
"""Collection of functions and function spaces"""
from __future__ import annotations
import typing
if typing.TYPE_CHECKING:
from dolfinx.mesh import Mesh
from functools import singledispatch
import numpy as np
import numpy.typing as npt
from dolfinx.fem import dofmap
from petsc4py import PETSc
import basix
import basix.ufl_wrapper
import ufl
import ufl.algorithms
import ufl.algorithms.analysis
from dolfinx import cpp as _cpp
from dolfinx import jit, la
from ufl.domain import extract_unique_domain
class Constant(ufl.Constant):
def __init__(self, domain, c: typing.Union[np.ndarray, typing.Sequence, float]):
"""A constant with respect to a domain.
Args:
domain: DOLFINx or UFL mesh
c: Value of the constant.
"""
c = np.asarray(c)
super().__init__(domain, c.shape)
try:
if c.dtype == np.complex64:
self._cpp_object = _cpp.fem.Constant_complex64(c)
elif c.dtype == np.complex128:
self._cpp_object = _cpp.fem.Constant_complex128(c)
elif c.dtype == np.float32:
self._cpp_object = _cpp.fem.Constant_float32(c)
elif c.dtype == np.float64:
self._cpp_object = _cpp.fem.Constant_float64(c)
else:
raise RuntimeError("Unsupported dtype")
except AttributeError:
raise AttributeError("Constant value must have a dtype attribute.")
@property
def value(self):
"""The value of the constant"""
return self._cpp_object.value
@value.setter
def value(self, v):
np.copyto(self._cpp_object.value, np.asarray(v))
@property
def dtype(self) -> np.dtype:
return self._cpp_object.dtype
def __float__(self):
if self.ufl_shape or self.ufl_free_indices:
raise TypeError(
"Cannot evaluate a nonscalar expression to a scalar value.")
else:
return float(self.value)
def __complex__(self):
if self.ufl_shape or self.ufl_free_indices:
raise TypeError(
"Cannot evaluate a nonscalar expression to a scalar value.")
else:
return complex(self.value)
class Expression:
def __init__(self, ufl_expression: ufl.core.expr.Expr, X: np.ndarray,
form_compiler_options: dict = {}, jit_options: dict = {},
dtype=PETSc.ScalarType):
"""Create DOLFINx Expression.
Represents a mathematical expression evaluated at a pre-defined
set of points on the reference cell. This class closely follows
the concept of a UFC Expression.
This functionality can be used to evaluate a gradient of a
Function at the quadrature points in all cells. This evaluated
gradient can then be used as input to a non-FEniCS function that
calculates a material constitutive model.
Args:
ufl_expression: Pure UFL expression
X: Array of points of shape `(num_points, tdim)` on the
reference element.
form_compiler_options: Options used in FFCx compilation of
this Expression. Run ``ffcx --help`` in the commandline
to see all available options.
jit_options: Options controlling JIT compilation of C code.
Notes:
This wrapper is responsible for the FFCx compilation of the
UFL Expr and attaching the correct data to the underlying
C++ Expression.
"""
assert X.ndim < 3
num_points = X.shape[0] if X.ndim == 2 else 1
_X = np.reshape(X, (num_points, -1))
mesh = extract_unique_domain(ufl_expression).ufl_cargo()
# Compile UFL expression with JIT
if dtype == np.float32:
form_compiler_options["scalar_type"] = "float"
if dtype == np.float64:
form_compiler_options["scalar_type"] = "double"
elif dtype == np.complex128:
form_compiler_options["scalar_type"] = "double _Complex"
else:
raise RuntimeError(
f"Unsupported scalar type {dtype} for Expression.")
self._ufcx_expression, module, self._code = jit.ffcx_jit(mesh.comm, (ufl_expression, _X),
form_compiler_options=form_compiler_options,
jit_options=jit_options)
self._ufl_expression = ufl_expression
# Prepare coefficients data. For every coefficient in form take
# its C++ object.
original_coefficients = ufl.algorithms.extract_coefficients(
ufl_expression)
coeffs = [original_coefficients[self._ufcx_expression.original_coefficient_positions[i]]._cpp_object
for i in range(self._ufcx_expression.num_coefficients)]
ufl_constants = ufl.algorithms.analysis.extract_constants(
ufl_expression)
constants = [constant._cpp_object for constant in ufl_constants]
arguments = ufl.algorithms.extract_arguments(ufl_expression)
if len(arguments) == 0:
self._argument_function_space = None
elif len(arguments) == 1:
self._argument_function_space = arguments[0].ufl_function_space(
)._cpp_object
else:
raise RuntimeError(
"Expressions with more that one Argument not allowed.")
def create_expression(dtype):
if dtype is np.float32:
return _cpp.fem.create_expression_float32
elif dtype is np.float64:
return _cpp.fem.create_expression_float64
elif dtype is np.complex128:
return _cpp.fem.create_expression_complex128
else:
raise NotImplementedError(f"Type {dtype} not supported.")
ffi = module.ffi
self._cpp_object = create_expression(dtype)(ffi.cast("uintptr_t", ffi.addressof(self._ufcx_expression)),
coeffs, constants, mesh, self.argument_function_space)
def eval(self, cells: np.ndarray, values: typing.Optional[np.ndarray] = None) -> np.ndarray:
"""Evaluate Expression in cells. Values should have shape
(cells.shape[0], num_points * value_size * num_all_argument_dofs).
If values is not passed then a new array will be allocated.
"""
_cells = np.asarray(cells, dtype=np.int32)
if self.argument_function_space is None:
argument_space_dimension = 1
else:
argument_space_dimension = self.argument_function_space.element.space_dimension
values_shape = (_cells.shape[0], self.X(
).shape[0] * self.value_size * argument_space_dimension)
# Allocate memory for result if u was not provided
if values is None:
values = np.zeros(values_shape, dtype=self.dtype)
else:
if values.shape != values_shape:
raise TypeError(
"Passed array values does not have correct shape.")
if values.dtype != self.dtype:
raise TypeError(
"Passed array values does not have correct dtype.")
self._cpp_object.eval(cells, values)
return values
def X(self) -> np.ndarray:
"""Evaluation points on the reference cell"""
return self._cpp_object.X()
@property
def ufl_expression(self):
"""Original UFL Expression"""
return self._ufl_expression
@property
def value_size(self) -> int:
"""Value size of the expression"""
return self._cpp_object.value_size
@property
def argument_function_space(self) -> typing.Optional[FunctionSpace]:
"""The argument function space if expression has argument"""
return self._argument_function_space
@property
def ufcx_expression(self):
"""The compiled ufcx_expression object"""
return self._ufcx_expression
@property
def code(self) -> str:
"""C code strings"""
return self._code
@property
def dtype(self) -> np.dtype:
return self._cpp_object.dtype
class Function(ufl.Coefficient):
"""A finite element function that is represented by a function space
(domain, element and dofmap) and a vector holding the
degrees-of-freedom
"""
def __init__(self, V: FunctionSpace, x: typing.Optional[la.VectorMetaClass] = None,
name: typing.Optional[str] = None, dtype: np.dtype = PETSc.ScalarType):
"""Initialize a finite element Function.
Args:
V: The function space that the Function is defined on.
x: Function degree-of-freedom vector. Typically required
only when reading a saved Function from file.
name: Function name.
dtype: Scalar type.
"""
# Create cpp Function
def functiontype(dtype):
if dtype == np.dtype(np.float32):
return _cpp.fem.Function_float32
elif dtype == np.dtype(np.float64):
return _cpp.fem.Function_float64
elif dtype == np.dtype(np.complex64):
return _cpp.fem.Function_complex64
elif dtype == np.dtype(np.complex128):
return _cpp.fem.Function_complex128
else:
raise NotImplementedError(f"Type {dtype} not supported.")
if x is not None:
self._cpp_object = functiontype(dtype)(V._cpp_object, x)
else:
self._cpp_object = functiontype(dtype)(V._cpp_object)
# Initialize the ufl.FunctionSpace
super().__init__(V.ufl_function_space())
# Set name
if name is None:
self.name = "f"
else:
self.name = name
# Store DOLFINx FunctionSpace object
self._V = V
# PETSc Vec wrapper around the C++ function data. Constructed
# when first requested.
self._petsc_x = None
@property
def function_space(self) -> FunctionSpace:
"""The FunctionSpace that the Function is defined on"""
return self._V
def eval(self, x: npt.ArrayLike, cells: npt.ArrayLike, u=None) -> np.ndarray:
"""Evaluate Function at points x, where x has shape (num_points, 3),
and cells has shape (num_points,) and cell[i] is the index of the
cell containing point x[i]. If the cell index is negative the
point is ignored."""
# Make sure input coordinates are a NumPy array
_x = np.asarray(x, dtype=np.float64)
assert _x.ndim < 3
if len(_x) == 0:
_x = np.zeros((0, 3))
else:
shape0 = _x.shape[0] if _x.ndim == 2 else 1
_x = np.reshape(_x, (shape0, -1))
num_points = _x.shape[0]
if _x.shape[1] != 3:
raise ValueError(
"Coordinate(s) for Function evaluation must have length 3.")
# Make sure cells are a NumPy array
_cells = np.asarray(cells, dtype=np.int32)
assert _cells.ndim < 2
num_points_c = _cells.shape[0] if _cells.ndim == 1 else 1
_cells = np.reshape(_cells, num_points_c)
# Allocate memory for return value if not provided
if u is None:
value_size = ufl.product(self.ufl_element().value_shape())
if np.issubdtype(PETSc.ScalarType, np.complexfloating):
u = np.empty((num_points, value_size), dtype=np.complex128)
else:
u = np.empty((num_points, value_size))
self._cpp_object.eval(_x, _cells, u)
if num_points == 1:
u = np.reshape(u, (-1, ))
return u
def interpolate(self, u: typing.Union[typing.Callable, Expression, Function],
cells: typing.Optional[np.ndarray] = None) -> None:
"""Interpolate an expression
Args:
u: The function, Expression or Function to interpolate.
cells: The cells to interpolate over. If `None` then all
cells are interpolated over.
"""
@singledispatch
def _interpolate(u, cells: typing.Optional[np.ndarray] = None):
"""Interpolate a cpp.fem.Function"""
self._cpp_object.interpolate(u, cells)
@_interpolate.register(Function)
def _(u: Function, cells: typing.Optional[np.ndarray] = None):
"""Interpolate a fem.Function"""
self._cpp_object.interpolate(u._cpp_object, cells)
@_interpolate.register(int)
def _(u_ptr: int, cells: typing.Optional[np.ndarray] = None):
"""Interpolate using a pointer to a function f(x)"""
self._cpp_object.interpolate_ptr(u_ptr, cells)
@_interpolate.register(Expression)
def _(expr: Expression, cells: typing.Optional[np.ndarray] = None):
"""Interpolate Expression for the set of cells"""
self._cpp_object.interpolate(expr._cpp_object, cells)
if cells is None:
mesh = self.function_space.mesh
map = mesh.topology.index_map(mesh.topology.dim)
cells = np.arange(map.size_local + map.num_ghosts, dtype=np.int32)
try:
# u is a Function or Expression (or pointer to one)
_interpolate(u, cells)
except TypeError:
# u is callable
assert callable(u)
x = _cpp.fem.interpolation_coords(
self._V.element, self._V.mesh, cells)
self._cpp_object.interpolate(
np.asarray(u(x), dtype=self.dtype), cells)
def copy(self) -> Function:
"""Return a copy of the Function. The FunctionSpace is shared and the
degree-of-freedom vector is copied.
"""
return Function(self.function_space, type(self.x)(self.x))
@property
def vector(self):
"""PETSc vector holding the degrees-of-freedom."""
if self._petsc_x is None:
self._petsc_x = _cpp.la.petsc.create_vector_wrap(self.x)
return self._petsc_x
@property
def x(self):
"""Vector holding the degrees-of-freedom."""
return self._cpp_object.x
@property
def dtype(self) -> np.dtype:
return self._cpp_object.x.array.dtype
@property
def name(self) -> str:
"""Name of the Function."""
return self._cpp_object.name
@name.setter
def name(self, name):
self._cpp_object.name = name
def __str__(self):
"""Pretty print representation of it self."""
return self.name
def sub(self, i: int) -> Function:
"""Return a sub function.
Args:
i: The index of the sub-function to extract.
Note:
The sub functions are numbered i = 0..N-1, where N is the
total number of sub spaces.
"""
return Function(self._V.sub(i), self.x, name=f"{str(self)}_{i}")
def split(self) -> tuple[Function, ...]:
"""Extract any sub functions.
A sub function can be extracted from a discrete function that
is in a mixed, vector, or tensor FunctionSpace. The sub
function resides in the subspace of the mixed space.
Args:
Function space subspaces.
"""
num_sub_spaces = self.function_space.num_sub_spaces
if num_sub_spaces == 1:
raise RuntimeError("No subfunctions to extract")
return tuple(self.sub(i) for i in range(num_sub_spaces))
def collapse(self) -> Function:
u_collapsed = self._cpp_object.collapse()
V_collapsed = FunctionSpace(None, self.ufl_element(),
u_collapsed.function_space)
return Function(V_collapsed, u_collapsed.x)
class ElementMetaData(typing.NamedTuple):
"""Data for representing a finite element"""
family: str
degree: int
class FunctionSpace(ufl.FunctionSpace):
"""A space on which Functions (fields) can be defined."""
def __init__(self, mesh: typing.Union[None, Mesh],
element: typing.Union[ufl.FiniteElementBase, ElementMetaData, typing.Tuple[str, int]],
cppV: typing.Optional[_cpp.fem.FunctionSpace] = None,
form_compiler_options: dict[str, typing.Any] = {}, jit_options: dict[str, typing.Any] = {}):
"""Create a finite element function space."""
# Create function space from a UFL element and existing cpp
# FunctionSpace
if cppV is not None:
assert mesh is None
ufl_domain = cppV.mesh.ufl_domain()
super().__init__(ufl_domain, element)
self._cpp_object = cppV
return
if mesh is not None:
assert cppV is None
# Initialise the ufl.FunctionSpace
if isinstance(element, ufl.FiniteElementBase):
super().__init__(mesh.ufl_domain(), element)
else:
e = ElementMetaData(*element)
ufl_element = basix.ufl_wrapper.create_element(
e.family, mesh.ufl_cell().cellname(), e.degree, gdim=mesh.ufl_cell().geometric_dimension())
super().__init__(mesh.ufl_domain(), ufl_element)
# Compile dofmap and element and create DOLFIN objects
(self._ufcx_element, self._ufcx_dofmap), module, code = jit.ffcx_jit(
mesh.comm, self.ufl_element(), form_compiler_options=form_compiler_options,
jit_options=jit_options)
ffi = module.ffi
cpp_element = _cpp.fem.FiniteElement(
ffi.cast("uintptr_t", ffi.addressof(self._ufcx_element)))
cpp_dofmap = _cpp.fem.create_dofmap(mesh.comm, ffi.cast(
"uintptr_t", ffi.addressof(self._ufcx_dofmap)), mesh.topology, cpp_element)
# Initialize the cpp.FunctionSpace
self._cpp_object = _cpp.fem.FunctionSpace(
mesh, cpp_element, cpp_dofmap)
def clone(self) -> FunctionSpace:
"""Return a new FunctionSpace :math:`W` which shares data with this
FunctionSpace :math:`V`, but with a different unique integer ID.
This function is helpful for defining mixed problems and using
blocked linear algebra. For example, a matrix block defined on
the spaces :math:`V \\times W` where, :math:`V` and :math:`W`
are defined on the same finite element and mesh can be
identified as an off-diagonal block whereas the :math:`V \\times
V` and :math:`V \\times V` matrices can be identified as
diagonal blocks. This is relevant for the handling of boundary
conditions.
"""
Vcpp = _cpp.fem.FunctionSpace(
self._cpp_object.mesh, self._cpp_object.element, self._cpp_object.dofmap)
return FunctionSpace(None, self.ufl_element(), Vcpp)
@property
def num_sub_spaces(self) -> int:
"""Number of sub spaces"""
return self.element.num_sub_elements
def sub(self, i: int) -> FunctionSpace:
"""Return the i-th sub space.
Args:
i: The subspace index
Returns:
A subspace
"""
assert self.ufl_element().num_sub_elements() > i
sub_element = self.ufl_element().sub_elements()[i]
cppV_sub = self._cpp_object.sub([i])
return FunctionSpace(None, sub_element, cppV_sub)
def component(self):
"""Return the component relative to the parent space."""
return self._cpp_object.component()
def contains(self, V) -> bool:
"""Check if a space is contained in, or is the same as (identity), this space.
Args:
V: The space to check to for inclusion.
Returns:
True is ``V`` is contained in, or is the same as, this space
"""
return self._cpp_object.contains(V._cpp_object)
def __eq__(self, other):
"""Comparison for equality."""
return super().__eq__(other) and self._cpp_object == other._cpp_object
def __ne__(self, other):
"""Comparison for inequality."""
return super().__ne__(other) or self._cpp_object != other._cpp_object
def ufl_cell(self):
return self._cpp_object.mesh.ufl_cell()
def ufl_function_space(self) -> ufl.FunctionSpace:
"""UFL function space"""
return self
@property
def element(self):
return self._cpp_object.element
@property
def dofmap(self) -> dofmap.DofMap:
"""Degree-of-freedom map associated with the function space."""
return dofmap.DofMap(self._cpp_object.dofmap)
@property
def mesh(self) -> _cpp.mesh.Mesh:
"""Return the mesh on which the function space is defined."""
return self._cpp_object.mesh
def collapse(self) -> tuple[FunctionSpace, np.ndarray]:
"""Collapse a subspace and return a new function space and a map from
new to old dofs.
Returns:
The new function space and the map from new to old degrees-of-freedom.
"""
cpp_space, dofs = self._cpp_object.collapse()
V = FunctionSpace(None, self.ufl_element(), cpp_space)
return V, dofs
def tabulate_dof_coordinates(self) -> np.ndarray:
return self._cpp_object.tabulate_dof_coordinates()
def VectorFunctionSpace(mesh: Mesh, element: typing.Union[ElementMetaData, typing.Tuple[str, int]], dim=None,
restriction=None) -> FunctionSpace:
"""Create vector finite element (composition of scalar elements) function space."""
e = ElementMetaData(*element)
ufl_element = basix.ufl_wrapper.create_vector_element(
e.family, mesh.ufl_cell().cellname(), e.degree, dim=dim,
gdim=mesh.geometry.dim)
return FunctionSpace(mesh, ufl_element)
def TensorFunctionSpace(mesh: Mesh, element: typing.Union[ElementMetaData, typing.Tuple[str, int]], shape=None,
symmetry: typing.Optional[bool] = None, restriction=None) -> FunctionSpace:
"""Create tensor finite element (composition of scalar elements) function space."""
e = ElementMetaData(*element)
ufl_element = basix.ufl_wrapper.create_tensor_element(
e.family, mesh.ufl_cell().cellname(), e.degree, shape=shape, symmetry=symmetry,
gdim=mesh.geometry.dim)
return FunctionSpace(mesh, ufl_element)