def point_evaluation(self, order, point, entity=None):
entities = self._factor_entity(entity)
entity_dim, _ = zip(*entities)
# Split point expression
assert len(self.cell.cells) == len(entity_dim)
point_dims = [
cell.construct_subelement(dim).get_spatial_dimension()
for cell, dim in zip(self.cell.cells, entity_dim)
]
assert isinstance(point,
gem.Node) and point.shape == (sum(point_dims), )
slices = TensorProductCell._split_slices(point_dims)
point_factors = []
for s in slices:
point_factors.append(
gem.ListTensor([
gem.Indexed(point, (i, )) for i in range(s.start, s.stop)
]))
# Subelement results
factor_results = [
fe.point_evaluation(order, p_, e)
for fe, p_, e in zip(self.factors, point_factors, entities)
]
return self._merge_evaluations(factor_results)
def point_evaluation_ciarlet(fiat_element, order, refcoords, entity):
# Coordinates on the reference entity (SymPy)
esd, = refcoords.shape
Xi = sp.symbols('X Y Z')[:esd]
# Coordinates on the reference cell
cell = fiat_element.get_reference_element()
X = cell.get_entity_transform(*entity)(Xi)
# Evaluate expansion set at SymPy point
poly_set = fiat_element.get_nodal_basis()
degree = poly_set.get_embedded_degree()
base_values = poly_set.get_expansion_set().tabulate(degree, [X])
m = len(base_values)
assert base_values.shape == (m, 1)
base_values_sympy = np.array(list(base_values.flat))
# Find constant polynomials
def is_const(expr):
try:
float(expr)
return True
except TypeError:
return False
const_mask = np.array(list(map(is_const, base_values_sympy)))
# Convert SymPy expression to GEM
mapper = gem.node.Memoizer(sympy2gem)
mapper.bindings = {
s: gem.Indexed(refcoords, (i, ))
for i, s in enumerate(Xi)
}
base_values = gem.ListTensor(list(map(mapper, base_values.flat)))
# Populate result dict, creating precomputed coefficient
# matrices for each derivative tuple.
result = {}
for i in range(order + 1):
for alpha in mis(cell.get_spatial_dimension(), i):
D = form_matrix_product(poly_set.get_dmats(), alpha)
table = np.dot(poly_set.get_coeffs(), np.transpose(D))
assert table.shape[-1] == m
zerocols = np.isclose(
abs(table).max(axis=tuple(range(table.ndim - 1))), 0.0)
if all(np.logical_or(const_mask, zerocols)):
# Casting is safe by assertion of is_const
vals = base_values_sympy[const_mask].astype(np.float64)
result[alpha] = gem.Literal(table[..., const_mask].dot(vals))
else:
beta = tuple(gem.Index() for s in table.shape[:-1])
k = gem.Index()
result[alpha] = gem.ComponentTensor(
gem.IndexSum(
gem.Product(
gem.Indexed(gem.Literal(table), beta + (k, )),
gem.Indexed(base_values, (k, ))), (k, )), beta)
return result
def physical_tangents(self):
rts = [self.interface.fiat_cell.compute_tangents(1, f)[0] for f in range(3)]
jac = self.jacobian_at([1/3, 1/3])
els = self.physical_edge_lengths()
return gem.ListTensor([[(jac[0, 0]*rts[i][0] + jac[0, 1]*rts[i][1]) / els[i],
(jac[1, 0]*rts[i][0] + jac[1, 1]*rts[i][1]) / els[i]]
for i in range(3)])
def basis_evaluation(self, order, ps, entity=None):
'''Return code for evaluating the element at known points on the
reference element.
:param order: return derivatives up to this order.
:param ps: the point set.
:param entity: the cell entity on which to tabulate.
'''
space_dimension = self._element.space_dimension()
value_size = np.prod(self._element.value_shape(), dtype=int)
fiat_result = self._element.tabulate(order, ps.points, entity)
result = {}
for alpha, fiat_table in iteritems(fiat_result):
if isinstance(fiat_table, Exception):
result[alpha] = gem.Failure(
self.index_shape + self.value_shape, fiat_table)
continue
derivative = sum(alpha)
table_roll = fiat_table.reshape(space_dimension, value_size,
len(ps.points)).transpose(1, 2, 0)
exprs = []
for table in table_roll:
if derivative < self.degree:
point_indices = ps.indices
point_shape = tuple(index.extent
for index in point_indices)
exprs.append(
gem.partial_indexed(
gem.Literal(
table.reshape(point_shape + self.index_shape)),
point_indices))
elif derivative == self.degree:
# Make sure numerics satisfies theory
assert np.allclose(table, table.mean(axis=0,
keepdims=True))
exprs.append(gem.Literal(table[0]))
else:
# Make sure numerics satisfies theory
assert np.allclose(table, 0.0)
exprs.append(gem.Zero(self.index_shape))
if self.value_shape:
beta = self.get_indices()
zeta = self.get_value_indices()
result[alpha] = gem.ComponentTensor(
gem.Indexed(
gem.ListTensor(
np.array([
gem.Indexed(expr, beta) for expr in exprs
]).reshape(self.value_shape)), zeta), beta + zeta)
else:
expr, = exprs
result[alpha] = expr
return result
def point_evaluation_generic(fiat_element, order, refcoords, entity):
# Coordinates on the reference entity (SymPy)
esd, = refcoords.shape
Xi = sp.symbols('X Y Z')[:esd]
space_dimension = fiat_element.space_dimension()
value_size = np.prod(fiat_element.value_shape(), dtype=int)
fiat_result = fiat_element.tabulate(order, [Xi], entity)
result = {}
for alpha, fiat_table in fiat_result.items():
if isinstance(fiat_table, Exception):
result[alpha] = gem.Failure(
(space_dimension, ) + fiat_element.value_shape(), fiat_table)
continue
# Convert SymPy expression to GEM
mapper = gem.node.Memoizer(sympy2gem)
mapper.bindings = {
s: gem.Indexed(refcoords, (i, ))
for i, s in enumerate(Xi)
}
gem_table = np.vectorize(mapper)(fiat_table)
table_roll = gem_table.reshape(space_dimension, value_size).transpose()
exprs = []
for table in table_roll:
exprs.append(gem.ListTensor(table.reshape(space_dimension)))
if fiat_element.value_shape():
beta = (gem.Index(extent=space_dimension), )
zeta = tuple(
gem.Index(extent=d) for d in fiat_element.value_shape())
result[alpha] = gem.ComponentTensor(
gem.Indexed(
gem.ListTensor(
np.array([gem.Indexed(expr, beta) for expr in exprs
]).reshape(fiat_element.value_shape())),
zeta), beta + zeta)
else:
expr, = exprs
result[alpha] = expr
return result
def dual_basis(self):
Q, x = self.wrappee.dual_basis
beta = self.get_indices()
zeta = self.get_value_indices()
# Index out the basis indices from wrapee's Q, to get
# something of wrappee.value_shape, then promote to new shape
# with the same transform as done for basis evaluation
Q = gem.ListTensor(self.transform(gem.partial_indexed(Q, beta)))
# Finally wrap up Q in shape again (now with some extra
# value_shape indices)
return gem.ComponentTensor(Q[zeta], beta + zeta), x
def translate_cell_edge_vectors(terminal, mt, ctx):
# WARNING: Assumes straight edges!
coords = CellVertices(terminal.ufl_domain())
ufl_expr = construct_modified_terminal(mt, coords)
cell_vertices = ctx.translator(ufl_expr)
e = gem.Index()
c = gem.Index()
expr = gem.ListTensor([
gem.Sum(
gem.Indexed(cell_vertices, (u, c)),
gem.Product(gem.Literal(-1), gem.Indexed(cell_vertices, (v, c))))
for _, (u, v) in sorted(ctx.fiat_cell.get_topology()[1].items())
])
return gem.ComponentTensor(gem.Indexed(expr, (e, )), (e, c))
def physical_tangents(self):
if not (isinstance(self.interface.fiat_cell, UFCSimplex)
and self.interface.fiat_cell.get_spatial_dimension() == 2):
raise NotImplementedError("Only works for triangles for now")
rts = [
self.interface.fiat_cell.compute_tangents(1, f)[0]
for f in range(3)
]
jac = self.jacobian_at([1 / 3, 1 / 3])
els = self.physical_edge_lengths()
return gem.ListTensor([[
(jac[0, 0] * rts[i][0] + jac[0, 1] * rts[i][1]) / els[i],
(jac[1, 0] * rts[i][0] + jac[1, 1] * rts[i][1]) / els[i]
] for i in range(3)])
def fiat_to_ufl(fiat_dict, order):
# All derivative multiindices must be of the same dimension.
dimension, = set(len(alpha) for alpha in fiat_dict.keys())
# All derivative tables must have the same shape.
shape, = set(table.shape for table in fiat_dict.values())
sigma = tuple(gem.Index(extent=extent) for extent in shape)
# Convert from FIAT to UFL format
eye = numpy.eye(dimension, dtype=int)
tensor = numpy.empty((dimension, ) * order, dtype=object)
for multiindex in numpy.ndindex(tensor.shape):
alpha = tuple(eye[multiindex, :].sum(axis=0))
tensor[multiindex] = gem.Indexed(fiat_dict[alpha], sigma)
delta = tuple(gem.Index(extent=dimension) for _ in range(order))
if order > 0:
tensor = gem.Indexed(gem.ListTensor(tensor), delta)
else:
tensor = tensor[()]
return gem.ComponentTensor(tensor, sigma + delta)
def basis_evaluation(self,
order,
ps,
entity=None,
coordinate_mapping=None):
'''Return code for evaluating the element at known points on the
reference element.
:param order: return derivatives up to this order.
:param ps: the point set.
:param entity: the cell entity on which to tabulate.
'''
# Build everything in sympy
vs, xx, _ = self._basis
# and convert -- all this can be used for each derivative!
phys_verts = coordinate_mapping.physical_vertices()
phys_points = gem.partial_indexed(
coordinate_mapping.physical_points(ps, entity=entity), ps.indices)
repl = dict(
(vs[idx], phys_verts[idx]) for idx in numpy.ndindex(vs.shape))
repl.update(zip(xx, phys_points))
mapper = gem.node.Memoizer(sympy2gem)
mapper.bindings = repl
result = {}
for i in range(order + 1):
alphas = mis(2, i)
for alpha in alphas:
dphis = self._basis_deriv(xx, alpha)
result[alpha] = gem.ListTensor(list(map(mapper, dphis)))
return result
def basis_evaluation(self,
order,
ps,
entity=None,
coordinate_mapping=None):
'''Return code for evaluating the element at known points on the
reference element.
:param order: return derivatives up to this order.
:param ps: the point set.
:param entity: the cell entity on which to tabulate.
'''
space_dimension = self._element.space_dimension()
value_size = np.prod(self._element.value_shape(), dtype=int)
fiat_result = self._element.tabulate(order, ps.points, entity)
result = {}
# In almost all cases, we have
# self.space_dimension() == self._element.space_dimension()
# But for Bell, FIAT reports 21 basis functions,
# but FInAT only 18 (because there are actually 18
# basis functions, and the additional 3 are for
# dealing with transformations between physical
# and reference space).
index_shape = (self._element.space_dimension(), )
for alpha, fiat_table in fiat_result.items():
if isinstance(fiat_table, Exception):
result[alpha] = gem.Failure(
self.index_shape + self.value_shape, fiat_table)
continue
derivative = sum(alpha)
table_roll = fiat_table.reshape(space_dimension, value_size,
len(ps.points)).transpose(1, 2, 0)
exprs = []
for table in table_roll:
if derivative < self.degree:
point_indices = ps.indices
point_shape = tuple(index.extent
for index in point_indices)
exprs.append(
gem.partial_indexed(
gem.Literal(
table.reshape(point_shape + index_shape)),
point_indices))
elif derivative == self.degree:
# Make sure numerics satisfies theory
exprs.append(gem.Literal(table[0]))
else:
# Make sure numerics satisfies theory
assert np.allclose(table, 0.0)
exprs.append(gem.Zero(self.index_shape))
if self.value_shape:
# As above, this extent may be different from that
# advertised by the finat element.
beta = tuple(gem.Index(extent=i) for i in index_shape)
assert len(beta) == len(self.get_indices())
zeta = self.get_value_indices()
result[alpha] = gem.ComponentTensor(
gem.Indexed(
gem.ListTensor(
np.array([
gem.Indexed(expr, beta) for expr in exprs
]).reshape(self.value_shape)), zeta), beta + zeta)
else:
expr, = exprs
result[alpha] = expr
return result
def physical_normals(self):
pts = self.physical_tangents()
return gem.ListTensor([[pts[i, 1], -1 * pts[i, 0]] for i in range(3)])
def promote(table):
v = gem.partial_indexed(table, beta)
u = gem.ListTensor(self.transform(v))
return gem.ComponentTensor(gem.Indexed(u, zeta), beta + zeta)
def expression(self):
result = []
for point_set in self.factors:
for i in range(point_set.dimension):
result.append(gem.Indexed(point_set.expression, (i,)))
return gem.ListTensor(result)
def compile_expression_dual_evaluation(expression,
to_element,
*,
domain=None,
interface=None,
parameters=None,
coffee=False):
"""Compile a UFL expression to be evaluated against a compile-time known reference element's dual basis.
Useful for interpolating UFL expressions into e.g. N1curl spaces.
:arg expression: UFL expression
:arg to_element: A FInAT element for the target space
:arg domain: optional UFL domain the expression is defined on (required when expression contains no domain).
:arg interface: backend module for the kernel interface
:arg parameters: parameters object
:arg coffee: compile coffee kernel instead of loopy kernel
"""
import coffee.base as ast
import loopy as lp
# Just convert FInAT element to FIAT for now.
# Dual evaluation in FInAT will bring a thorough revision.
to_element = to_element.fiat_equivalent
if any(len(dual.deriv_dict) != 0 for dual in to_element.dual_basis()):
raise NotImplementedError(
"Can only interpolate onto dual basis functionals without derivative evaluation, sorry!"
)
if parameters is None:
parameters = default_parameters()
else:
_ = default_parameters()
_.update(parameters)
parameters = _
# Determine whether in complex mode
complex_mode = is_complex(parameters["scalar_type"])
# Find out which mapping to apply
try:
mapping, = set(to_element.mapping())
except ValueError:
raise NotImplementedError(
"Don't know how to interpolate onto zany spaces, sorry")
expression = apply_mapping(expression, mapping, domain)
# Apply UFL preprocessing
expression = ufl_utils.preprocess_expression(expression,
complex_mode=complex_mode)
# Initialise kernel builder
if interface is None:
if coffee:
import tsfc.kernel_interface.firedrake as firedrake_interface_coffee
interface = firedrake_interface_coffee.ExpressionKernelBuilder
else:
# Delayed import, loopy is a runtime dependency
import tsfc.kernel_interface.firedrake_loopy as firedrake_interface_loopy
interface = firedrake_interface_loopy.ExpressionKernelBuilder
builder = interface(parameters["scalar_type"])
arguments = extract_arguments(expression)
argument_multiindices = tuple(
builder.create_element(arg.ufl_element()).get_indices()
for arg in arguments)
# Replace coordinates (if any) unless otherwise specified by kwarg
if domain is None:
domain = expression.ufl_domain()
assert domain is not None
# Collect required coefficients
first_coefficient_fake_coords = False
coefficients = extract_coefficients(expression)
if has_type(expression, GeometricQuantity) or any(
fem.needs_coordinate_mapping(c.ufl_element())
for c in coefficients):
# Create a fake coordinate coefficient for a domain.
coords_coefficient = ufl.Coefficient(
ufl.FunctionSpace(domain, domain.ufl_coordinate_element()))
builder.domain_coordinate[domain] = coords_coefficient
builder.set_cell_sizes(domain)
coefficients = [coords_coefficient] + coefficients
first_coefficient_fake_coords = True
builder.set_coefficients(coefficients)
# Split mixed coefficients
expression = ufl_utils.split_coefficients(expression,
builder.coefficient_split)
# Translate to GEM
kernel_cfg = dict(
interface=builder,
ufl_cell=domain.ufl_cell(),
# FIXME: change if we ever implement
# interpolation on facets.
integral_type="cell",
argument_multiindices=argument_multiindices,
index_cache={},
scalar_type=parameters["scalar_type"])
if all(
isinstance(dual, PointEvaluation)
for dual in to_element.dual_basis()):
# This is an optimisation for point-evaluation nodes which
# should go away once FInAT offers the interface properly
qpoints = []
# Everything is just a point evaluation.
for dual in to_element.dual_basis():
ptdict = dual.get_point_dict()
qpoint, = ptdict.keys()
(qweight, component), = ptdict[qpoint]
assert allclose(qweight, 1.0)
assert component == ()
qpoints.append(qpoint)
point_set = PointSet(qpoints)
config = kernel_cfg.copy()
config.update(point_set=point_set)
# Allow interpolation onto QuadratureElements to refer to the quadrature
# rule they represent
if isinstance(to_element, FIAT.QuadratureElement):
assert allclose(asarray(qpoints), asarray(to_element._points))
quad_rule = QuadratureRule(point_set, to_element._weights)
config["quadrature_rule"] = quad_rule
expr, = fem.compile_ufl(expression, **config, point_sum=False)
# In some cases point_set.indices may be dropped from expr, but nothing
# new should now appear
assert set(expr.free_indices) <= set(
chain(point_set.indices, *argument_multiindices))
shape_indices = tuple(gem.Index() for _ in expr.shape)
basis_indices = point_set.indices
ir = gem.Indexed(expr, shape_indices)
else:
# This is general code but is more unrolled than necssary.
dual_expressions = [] # one for each functional
broadcast_shape = len(expression.ufl_shape) - len(
to_element.value_shape())
shape_indices = tuple(gem.Index()
for _ in expression.ufl_shape[:broadcast_shape])
expr_cache = {} # Sharing of evaluation of the expression at points
for dual in to_element.dual_basis():
pts = tuple(sorted(dual.get_point_dict().keys()))
try:
expr, point_set = expr_cache[pts]
except KeyError:
point_set = PointSet(pts)
config = kernel_cfg.copy()
config.update(point_set=point_set)
expr, = fem.compile_ufl(expression, **config, point_sum=False)
# In some cases point_set.indices may be dropped from expr, but
# nothing new should now appear
assert set(expr.free_indices) <= set(
chain(point_set.indices, *argument_multiindices))
expr = gem.partial_indexed(expr, shape_indices)
expr_cache[pts] = expr, point_set
weights = collections.defaultdict(list)
for p in pts:
for (w, cmp) in dual.get_point_dict()[p]:
weights[cmp].append(w)
qexprs = gem.Zero()
for cmp in sorted(weights):
qweights = gem.Literal(weights[cmp])
qexpr = gem.Indexed(expr, cmp)
qexpr = gem.index_sum(
gem.Indexed(qweights, point_set.indices) * qexpr,
point_set.indices)
qexprs = gem.Sum(qexprs, qexpr)
assert qexprs.shape == ()
assert set(qexprs.free_indices) == set(
chain(shape_indices, *argument_multiindices))
dual_expressions.append(qexprs)
basis_indices = (gem.Index(), )
ir = gem.Indexed(gem.ListTensor(dual_expressions), basis_indices)
# Build kernel body
return_indices = basis_indices + shape_indices + tuple(
chain(*argument_multiindices))
return_shape = tuple(i.extent for i in return_indices)
return_var = gem.Variable('A', return_shape)
if coffee:
return_arg = ast.Decl(parameters["scalar_type"],
ast.Symbol('A', rank=return_shape))
else:
return_arg = lp.GlobalArg("A",
dtype=parameters["scalar_type"],
shape=return_shape)
return_expr = gem.Indexed(return_var, return_indices)
# TODO: one should apply some GEM optimisations as in assembly,
# but we don't for now.
ir, = impero_utils.preprocess_gem([ir])
impero_c = impero_utils.compile_gem([(return_expr, ir)], return_indices)
index_names = dict(
(idx, "p%d" % i) for (i, idx) in enumerate(basis_indices))
# Handle kernel interface requirements
builder.register_requirements([ir])
# Build kernel tuple
return builder.construct_kernel(return_arg, impero_c, index_names,
first_coefficient_fake_coords)
def test_listtensor(protocol):
expr = gem.ListTensor([gem.Variable('x', ()), gem.Zero()])
unpickled = pickle.loads(pickle.dumps(expr, protocol))
assert expr == unpickled
