def assign(self, expr, subset=None):
"""Set the :class:`Function` value to the pointwise value of
expr. expr may only contain :class:`Function`\s on the same
:class:`.FunctionSpace` as the :class:`Function` being assigned to.
Similar functionality is available for the augmented assignment
operators `+=`, `-=`, `*=` and `/=`. For example, if `f` and `g` are
both Functions on the same :class:`.FunctionSpace` then::
f += 2 * g
will add twice `g` to `f`.
If present, subset must be an :class:`pyop2.Subset` of this
:class:`Function`'s ``node_set``. The expression will then
only be assigned to the nodes on that subset.
"""
if isinstance(expr, Function) and \
expr.function_space() == self.function_space():
expr.dat.copy(self.dat, subset=subset)
return self
from firedrake import assemble_expressions
assemble_expressions.evaluate_expression(
assemble_expressions.Assign(self, expr), subset)
return self
def assign(self, expr, subset=None):
"""Set the :class:`Function` value to the pointwise value of
expr. expr may only contain :class:`Function`\s on the same
:class:`.FunctionSpace` as the :class:`Function` being assigned to.
Similar functionality is available for the augmented assignment
operators `+=`, `-=`, `*=` and `/=`. For example, if `f` and `g` are
both Functions on the same :class:`.FunctionSpace` then::
f += 2 * g
will add twice `g` to `f`.
If present, subset must be an :class:`pyop2.Subset` of this
:class:`Function`'s ``node_set``. The expression will then
only be assigned to the nodes on that subset.
"""
if isinstance(expr, Function) and \
expr.function_space() == self.function_space():
expr.dat.copy(self.dat, subset=subset)
return self
from firedrake import assemble_expressions
assemble_expressions.evaluate_expression(
assemble_expressions.Assign(self, expr), subset)
return self
def __idiv__(self, expr):
if np.isscalar(expr):
self.dat /= expr
return self
if isinstance(expr, Function) and expr._function_space == self._function_space:
self.dat /= expr.dat
return self
from firedrake import assemble_expressions
assemble_expressions.evaluate_expression(assemble_expressions.IDiv(self, expr))
return self
def __idiv__(self, expr):
if np.isscalar(expr):
self.dat /= expr
return self
if isinstance(expr, Function) and \
expr.function_space() == self.function_space():
self.dat /= expr.dat
return self
from firedrake import assemble_expressions
assemble_expressions.evaluate_expression(
assemble_expressions.IDiv(self, expr))
return self
def __imul__(self, expr):
if np.isscalar(expr):
self.dat *= expr
return self
if isinstance(expr, Function) and \
expr.function_space() == self.function_space():
self.dat *= expr.dat
return self
from firedrake import assemble_expressions
assemble_expressions.evaluate_expression(
assemble_expressions.IMul(self, expr))
return self
def __imul__(self, expr):
if np.isscalar(expr):
self.dat *= expr
return self
if isinstance(expr, Function) and \
expr._function_space == self._function_space:
self.dat *= expr.dat
return self
from firedrake import assemble_expressions
assemble_expressions.evaluate_expression(
assemble_expressions.IMul(self, expr))
return self
def __isub__(self, expr):
if np.isscalar(expr):
self.dat -= expr
return self
if isinstance(expr, vector.Vector):
expr = expr.function
if isinstance(expr, Function) and \
expr.function_space() == self.function_space():
self.dat -= expr.dat
return self
from firedrake import assemble_expressions
assemble_expressions.evaluate_expression(
assemble_expressions.ISub(self, expr))
return self
def test_expression_cache():
mesh = UnitSquareMesh(1, 1)
V = VectorFunctionSpace(mesh, "CG", 1)
W = TensorFunctionSpace(mesh, "CG", 1)
u = Function(V)
v = Function(V)
w = Function(W)
i, j = indices(2)
exprA = Assign(u, as_vector(2 * u[i], i))
exprB = Assign(u, as_vector(2 * u[j], j))
assert len(u._expression_cache) == 0
evaluate_expression(exprA)
assert exprA.fast_key in u._expression_cache
assert exprA.slow_key in u._expression_cache
assert exprB.fast_key not in u._expression_cache
assert exprB.slow_key in u._expression_cache
evaluate_expression(exprB)
assert exprB.fast_key in u._expression_cache
assert exprA.fast_key in u._expression_cache
assert exprB.slow_key == exprA.slow_key
assert len(u._expression_cache) == 3
u.assign(as_vector([1, 2]))
u.assign(as_vector(2 * u[i], i))
v.assign(as_vector(2 * u[j], j))
w.assign(as_tensor([[1, 2], [0, 3]]))
w.assign(as_tensor(w[i, j] + w[j, i], (i, j)))
u -= as_vector([2, 4])
assert u.dat.norm < 1e-15
v -= as_vector([4, 8])
assert v.dat.norm < 1e-15
w -= as_tensor([[2, 2], [2, 6]])
assert w.dat.norm < 1e-15
assert len(u._expression_cache) == 5