Bt = PP[0:V.dim(), V.dim():W.dim()]
d = spdiags(1.0 / Xdiag, 0, len(Xdiag), len(Xdiag))
dBt = (d * Bt).tocsr()
plt.spy(dBt)
plt.show()
BQB = Bt.transpose() * dBt
dBt = PETSc.Mat().createAIJ(size=dBt.shape,
csr=(dBt.indptr, dBt.indices, dBt.data))
print dBt.size
BQB = PETSc.Mat().createAIJ(size=BQB.tocsr().shape,
csr=(BQB.tocsr().indptr,
BQB.tocsr().indices,
BQB.tocsr().data))
# parameters['linear_algebra_backend'] = 'PETSc'
kspBQB = NSprecondSetup.Ksp(BQB)
elif Solver == "PCD":
N = FacetNormal(mesh)
h = CellSize(mesh)
h_avg = avg(h)
alpha = 10.0
gamma = 10.0
(pQ) = TrialFunction(Q)
(qQ) = TestFunction(Q)
print MU
Mass = assemble(inner(pQ, qQ) * dx)
L = MU * (inner(grad(qQ), grad(pQ)) * dx(mesh))
# O = - dot(u_k, n)*pQ('+')*qQ*ds(mesh) - dot(u_k, n)*(pQ('+') - pQ('-'))*qQ('-')*dS(mesh)
pp = Function(Q)
Mass = assemble(inner(u,v)*dx)
B = assemble(-div(v)*p*dx)
bc = DirichletBC(V,Expression(("0.0","0.0")), boundary)
#bc.apply(Mass)
Mass = Mass.sparray()
MassD = Mass.diagonal()
del Mass
B = B.sparray()
d = spdiags(1.0/MassD, 0, len(MassD), len(MassD))
QB = d*B
L = B.transpose()*QB
L = PETSc.Mat().createAIJ(size=L.shape,csr=(L.indptr, L.indices, L.data))
QB = PETSc.Mat().createAIJ(size=QB.shape,csr=(QB.indptr, QB.indices, QB.data))
KspL = NSsetup.Ksp(L)
elif Solver == "PCD":
(pQ) = TrialFunction(Q)
(qQ) = TestFunction(Q)
print MU
Mass = assemble(inner(pQ,qQ)*dx)
L = assemble(inner(grad(pQ),grad(qQ))*dx)
fp = MU*inner(grad(qQ), grad(pQ))*dx+inner((u_k[0]*grad(pQ)[0]+u_k[1]*grad(pQ)[1]),qQ)*dx + (1./2)*div(u_k)*inner(pQ,qQ)*dx - (1./2)*(u_k[0]*n[0]+u_k[1]*n[1])*inner(pQ,qQ)*ds
L = CP.Assemble(L)
Mass = CP.Assemble(Mass)
# print L
SolutionTime = 0
while eps > tol and iter < maxiter:
iter += 1
