def _product(thetas: ThetaType, operators: AffineExpansionStorage_DirichletBC):
# Detect BCs defined on the same boundary
combined = dict() # from (function space, boundary) to value
for (op_index, op) in enumerate(operators):
for bc in op:
key = bc.identifier()
if key not in combined:
combined[key] = list()
combined[key].append((bc, op_index))
# Sum them
output = ProductOutputDirichletBC()
for (key, item) in combined.items():
value = function_copy(item[0][0].value())
value.vector().zero()
for addend in item:
theta = float(thetas[addend[1]])
fun = addend[0].value()
value.vector().add_local(theta * fun.vector().get_local())
value.vector().apply("add")
args = list()
args.append(item[0][0].function_space())
args.append(value)
args.extend(item[0][0]._domain)
output.append(DirichletBC(*args, **item[0][0]._kwargs))
return ProductOutput(output)
def _product(thetas: ThetaType, operators: AffineExpansionStorage_Function):
output = function_copy(operators[0])
output.vector().zero()
for (theta, operator) in zip(thetas, operators):
theta = float(theta)
output.vector().add_local(theta * operator.vector().get_local())
output.vector().apply("add")
return ProductOutput(output)
def integrate(self):
vector_over_time = list()
for function in self._function_over_time:
vector_over_time.append(function.vector().get_local())
integrated_vector = simps(vector_over_time, dx=self._time_step_size, axis=0)
integrated_function = function_copy(self._function_over_time[0])
integrated_function.vector().zero()
integrated_function.vector().set_local(integrated_vector)
integrated_function.vector().apply("insert")
return integrated_function
