def __init__(self, u_, Space, bcs=[], name="div", method={}):
solver_type = method.get('solver_type', 'cg')
preconditioner_type = method.get('preconditioner_type', 'default')
solver_method = method.get('method', 'default')
low_memory_version = method.get('low_memory_version', False)
OasisFunction.__init__(self, div(u_), Space, bcs=bcs, name=name,
method=solver_method, solver_type=solver_type,
preconditioner_type=preconditioner_type)
Source = u_[0].function_space()
if not low_memory_version:
self.matvec = [[A_cache[(self.test * TrialFunction(Source).dx(i) * dx, ())], u_[i]]
for i in range(Space.mesh().geometry().dim())]
if solver_method.lower() == "gradient_matrix":
from fenicstools import compiled_gradient_module
DG = FunctionSpace(Space.mesh(), 'DG', 0)
G = assemble(TrialFunction(DG) * self.test * dx())
dg = Function(DG)
self.WGM = []
st = TrialFunction(Source)
for i in range(Space.mesh().geometry().dim()):
dP = assemble(st.dx(i) * TestFunction(DG) * dx)
A = Matrix(G)
self.WGM.append(compiled_gradient_module.compute_weighted_gradient_matrix(A, dP, dg))
def __init__(self, p_, Space, i=0, bcs=[], name="grad", method={}):
assert len(p_.ufl_shape) == 0
assert i >= 0 and i < Space.mesh().geometry().dim()
solver_type = method.get('solver_type', 'cg')
preconditioner_type = method.get('preconditioner_type', 'default')
solver_method = method.get('method', 'default')
low_memory_version = method.get('low_memory_version', False)
OasisFunction.__init__(self, p_.dx(i), Space, bcs=bcs, name=name,
method=solver_method, solver_type=solver_type,
preconditioner_type=preconditioner_type)
self.i = i
Source = p_.function_space()
if not low_memory_version:
self.matvec = [
A_cache[(self.test * TrialFunction(Source).dx(i) * dx, ())], p_]
if solver_method.lower() == "gradient_matrix":
from fenicstools import compiled_gradient_module
DG = FunctionSpace(Space.mesh(), 'DG', 0)
G = assemble(TrialFunction(DG) * self.test * dx())
dg = Function(DG)
dP = assemble(TrialFunction(p_.function_space()).dx(i)
* TestFunction(DG) * dx())
self.WGM = compiled_gradient_module.compute_weighted_gradient_matrix(
G, dP, dg)
def __init__(self, u_, Space, bcs=[], name="div", method={}):
solver_type = method.get('solver_type', 'cg')
preconditioner_type = method.get('preconditioner_type', 'default')
solver_method = method.get('method', 'default')
low_memory_version = method.get('low_memory_version', False)
OasisFunction.__init__(self,
div(u_),
Space,
bcs=bcs,
name=name,
method=solver_method,
solver_type=solver_type,
preconditioner_type=preconditioner_type)
Source = u_[0].function_space()
if not low_memory_version:
self.matvec = [[
A_cache[(self.test * TrialFunction(Source).dx(i) * dx, ())],
u_[i]
] for i in range(Space.mesh().geometry().dim())]
if solver_method.lower() == "gradient_matrix":
from fenicstools import compiled_gradient_module
DG = FunctionSpace(Space.mesh(), 'DG', 0)
G = assemble(TrialFunction(DG) * self.test * dx())
dg = Function(DG)
self.WGM = []
st = TrialFunction(Source)
for i in range(Space.mesh().geometry().dim()):
dP = assemble(st.dx(i) * TestFunction(DG) * dx)
A = Matrix(G)
self.WGM.append(
compiled_gradient_module.compute_weighted_gradient_matrix(
A, dP, dg))
def __init__(self, p_, Space, i=0, bcs=[], name="grad", method={}):
assert len(p_.ufl_shape) == 0
assert i >= 0 and i < Space.mesh().geometry().dim()
solver_type = method.get('solver_type', 'cg')
preconditioner_type = method.get('preconditioner_type', 'default')
solver_method = method.get('method', 'default')
low_memory_version = method.get('low_memory_version', False)
OasisFunction.__init__(self,
p_.dx(i),
Space,
bcs=bcs,
name=name,
method=solver_method,
solver_type=solver_type,
preconditioner_type=preconditioner_type)
self.i = i
Source = p_.function_space()
if not low_memory_version:
self.matvec = [
A_cache[(self.test * TrialFunction(Source).dx(i) * dx, ())], p_
]
if solver_method.lower() == "gradient_matrix":
from fenicstools import compiled_gradient_module
DG = FunctionSpace(Space.mesh(), 'DG', 0)
G = assemble(TrialFunction(DG) * self.test * dx())
dg = Function(DG)
dP = assemble(
TrialFunction(p_.function_space()).dx(i) * TestFunction(DG) *
dx())
self.WGM = compiled_gradient_module.compute_weighted_gradient_matrix(
G, dP, dg)
