def nonzero_values(function):
serialized_vector = Vector(MPI.COMM_SELF)
function.vector().gather(
serialized_vector, array(range(function.function_space().dim()),
"intc"))
indices = nonzero(serialized_vector.get_local())
return sort(serialized_vector.get_local()[indices])
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 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
