def to_dense(A):
"""
Convert a sparse matrix A to dense.
For debugging only.
"""
v = Vector()
A.init_vector(v)
mpi_comm = v.mpi_comm()
nprocs = MPI.size(mpi_comm)
if nprocs > 1:
raise Exception("to_dense is only serial")
if hasattr(A, "getrow"):
n = A.size(0)
m = A.size(1)
B = np.zeros((n, m), dtype=np.float64)
for i in range(0, n):
[j, val] = A.getrow(i)
B[i, j] = val
return B
else:
x = Vector()
Ax = Vector()
A.init_vector(x, 1)
A.init_vector(Ax, 0)
n = Ax.array().shape[0]
m = x.array().shape[0]
B = np.zeros((n, m), dtype=np.float64)
for i in range(0, m):
i_ind = np.array([i], dtype=np.intc)
x.set_local(np.ones(i_ind.shape), i_ind)
x.apply("sum_values")
A.mult(x, Ax)
B[:, i] = Ax.array()
x.set_local(np.zeros(i_ind.shape), i_ind)
x.apply("sum_values")
return B
def to_dense(A, mpi_comm = mpi_comm_world() ):
"""
Convert a sparse matrix A to dense.
For debugging only.
"""
v = Vector(mpi_comm)
A.init_vector(v)
nprocs = MPI.size(mpi_comm)
if nprocs > 1:
raise Exception("to_dense is only serial")
if hasattr(A, "getrow"):
n = A.size(0)
m = A.size(1)
B = np.zeros( (n,m), dtype=np.float64)
for i in range(0,n):
[j, val] = A.getrow(i)
B[i,j] = val
return B
else:
x = Vector(mpi_comm)
Ax = Vector(mpi_comm)
A.init_vector(x,1)
A.init_vector(Ax,0)
n = Ax.get_local().shape[0]
m = x.get_local().shape[0]
B = np.zeros( (n,m), dtype=np.float64)
for i in range(0,m):
i_ind = np.array([i], dtype=np.intc)
x.set_local(np.ones(i_ind.shape), i_ind)
x.apply("sum_values")
A.mult(x,Ax)
B[:,i] = Ax.get_local()
x.set_local(np.zeros(i_ind.shape), i_ind)
x.apply("sum_values")
return B
def gradab(self, m1, m2):
self.gradm1 = project(nabla_grad(m1), self.Vd)
self.gradm2 = project(nabla_grad(m2), self.Vd)
uwv00, uwv00ind = [], []
uwv10, uwv10ind = [], []
uwv01, uwv01ind = [], []
uwv11, uwv11ind = [], []
for ii, x in enumerate(self.x):
G = np.array([self.gradm1(x), self.gradm2(x)]).T
u, s, v = np.linalg.svd(G)
sqrts2eps = np.sqrt(s**2 + self.eps)
W = np.diag(s / sqrts2eps)
uwv = u.dot(W.dot(v))
uwv00.append(uwv[0][0])
uwv00ind.append(ii)
uwv10.append(uwv[1][0])
uwv10ind.append(ii)
uwv01.append(uwv[0][1])
uwv01ind.append(ii)
uwv11.append(uwv[1][1])
uwv11ind.append(ii)
Grad = Function(self.VV)
grad = Grad.vector()
rhsG = Vector()
self.Gx1test.init_vector(rhsG, 1)
rhsG.set_local(np.array(uwv00), np.array(uwv00ind, dtype=np.intc))
rhsG.apply('insert')
grad.axpy(1.0, self.Gx1test * rhsG)
rhsG.zero()
rhsG.set_local(np.array(uwv10), np.array(uwv10ind, dtype=np.intc))
rhsG.apply('insert')
grad.axpy(1.0, self.Gy1test * rhsG)
rhsG.set_local(np.array(uwv01), np.array(uwv01ind, dtype=np.intc))
rhsG.apply('insert')
grad.axpy(1.0, self.Gx2test * rhsG)
rhsG.set_local(np.array(uwv11), np.array(uwv11ind, dtype=np.intc))
rhsG.apply('insert')
grad.axpy(1.0, self.Gy2test * rhsG)
return grad * self.k
def get_diagonal(A, d):
"""
Compute the diagonal of the square operator A.
Use Solver2Operator if A^-1 is needed.
"""
ej, xj = Vector(), Vector()
A.init_vector(ej, 1)
A.init_vector(xj, 0)
g_size = ej.size()
d.zero()
for gid in xrange(g_size):
owns_gid = ej.owns_index(gid)
if owns_gid:
SetToOwnedGid(ej, gid, 1.)
ej.apply("insert")
A.mult(ej, xj)
if owns_gid:
val = GetFromOwnedGid(xj, gid)
SetToOwnedGid(d, gid, val)
SetToOwnedGid(ej, gid, 0.)
ej.apply("insert")
d.apply("insert")
def get_diagonal(A, d):
"""
Compute the diagonal of the square operator :math:`A`.
Use :code:`Solver2Operator` if :math:`A^{-1}` is needed.
"""
ej, xj = Vector(d.mpi_comm()), Vector(d.mpi_comm())
A.init_vector(ej, 1)
A.init_vector(xj, 0)
g_size = ej.size()
d.zero()
for gid in range(g_size):
owns_gid = ej.owns_index(gid)
if owns_gid:
SetToOwnedGid(ej, gid, 1.)
ej.apply("insert")
A.mult(ej, xj)
if owns_gid:
val = GetFromOwnedGid(xj, gid)
SetToOwnedGid(d, gid, val)
SetToOwnedGid(ej, gid, 0.)
ej.apply("insert")
d.apply("insert")
def get_diagonal(A, d):
"""
Compute the diagonal of the square operator :math:`A`.
Use :code:`Solver2Operator` if :math:`A^{-1}` is needed.
"""
ej, xj = Vector(d.mpi_comm()), Vector(d.mpi_comm())
A.init_vector(ej,1)
A.init_vector(xj,0)
g_size = ej.size()
d.zero()
for gid in range(g_size):
owns_gid = ej.owns_index(gid)
if owns_gid:
SetToOwnedGid(ej, gid, 1.)
ej.apply("insert")
A.mult(ej,xj)
if owns_gid:
val = GetFromOwnedGid(xj, gid)
SetToOwnedGid(d, gid, val)
SetToOwnedGid(ej, gid, 0.)
ej.apply("insert")
d.apply("insert")
