def generate_random(self):
# Generate random vectors
S = FunctionsList(self.V)
for _ in range(self.N):
b = RandomDolfinFunction(self.V)
S.enrich(b)
F = RandomDolfinFunction(self.V)
# Return
return (S, F)
def generate_random(self):
# Generate random vectors
v1 = RandomDolfinFunction(self.V).vector()
v2 = RandomDolfinFunction(self.V).vector()
k = RandomDolfinFunction(self.V)
# Generate random matrix
A = assemble(self.a(k))
# Return
return (v1, v2, A)
def generate_random(self):
# Generate random vectors
Z = BasisFunctionsMatrix(self.V)
Z.init("u")
for _ in range(self.N):
b = RandomDolfinFunction(self.V)
Z.enrich(b)
F = RandomDolfinFunction(self.V)
# Return
return (Z, F)
def generate_random(self):
# Generate random vectors
S = FunctionsList(self.V)
for _ in range(self.N):
b = RandomDolfinFunction(self.V)
S.enrich(b)
k = RandomDolfinFunction(self.V)
# Generate random matrix
A = assemble(self.a(k))
# Return
return (S, A)
def generate_random(self):
# Generate random vectors
Z = BasisFunctionsMatrix(self.V)
Z.init("u")
for i in range(self.N):
b = RandomDolfinFunction(self.V)
Z.enrich(b)
# Generate random matrix
k = RandomDolfinFunction(self.V)
A = assemble(self.a(k))
# Generate random function
z = RandomDolfinFunction(self.V)
# Return
return (Z, A, z.vector())
def generate_random(self):
# Generate random forcing
g = RandomDolfinFunction(self.V)
# Generate correspondingly residual and jacobian forms
r = self.r(self.u, g)
j = self.j(self.u, r)
# Prepare problem wrapper
class ProblemWrapper(NonlinearProblemWrapper):
# Residual and jacobian functions
def residual_eval(self_, solution):
return self.callback(r)
def jacobian_eval(self_, solution):
return self.callback(j)
# Define boundary condition
def bc_eval(self_):
return None
# Empty solution monitor
def monitor(self_, solution):
pass
problem_wrapper = ProblemWrapper()
# Return
return (r, j, problem_wrapper)
def generate_random(self):
f = ()
for i in range(self.Q):
# Generate random vector
g = RandomDolfinFunction(self.V)
# Generate random form
f += (self.f(g), )
F = AffineExpansionStorage(f)
# Genereate random theta
theta = RandomTuple(self.Q)
# Return
return (theta, F)
def generate_random(self):
a = ()
for i in range(self.Q):
# Generate random vector
k = RandomDolfinFunction(self.V)
# Generate random form
a += (self.a(k), )
A = AffineExpansionStorage(a)
# Genereate random theta
theta = RandomTuple(self.Q)
# Return
return (theta, A)
def generate_random(self):
# Generate random vectors
v1 = RandomDolfinFunction(self.V).vector()
v2 = RandomDolfinFunction(self.V).vector()
# Return
return (v1, v2)
def generate_random(self):
# Generate random rhs
g = RandomDolfinFunction(self.V)
# Return
return (self.a, self.f(g))
