def max(self, generator, postprocessor=None):
if postprocessor is None:
def postprocessor(value):
return value
if self.distributed_max:
local_list_indices = list(
range(self.mpi_comm.rank, len(self._list), self.mpi_comm.size))
# start from index rank and take steps of length equal to size
else:
local_list_indices = list(range(len(self._list)))
values = array(len(local_list_indices))
values_with_postprocessing = array(len(local_list_indices))
for i in range(len(local_list_indices)):
values[i] = generator(self._list[local_list_indices[i]])
values_with_postprocessing[i] = postprocessor(values[i])
if self.distributed_max:
local_i_max = argmax(values_with_postprocessing)
local_value_max = values[local_i_max]
(global_value_max,
global_i_max) = parallel_max(local_value_max,
local_list_indices[local_i_max],
postprocessor, self.mpi_comm)
assert isinstance(global_i_max, tuple)
assert len(global_i_max) == 1
global_i_max = global_i_max[0]
else:
global_i_max = argmax(values_with_postprocessing)
global_value_max = values[global_i_max]
return (global_value_max, global_i_max)
def _abs(vector: Vector.Type()):
# Note: PETSc offers VecAbs and VecMax, but for symmetry with the matrix case we do the same by hand
vec = to_petsc4py(vector)
row_start, row_end = vec.getOwnershipRange()
i_max = None
value_max = None
for i in range(row_start, row_end):
val = vec.getValue(i)
if value_max is None or fabs(val) > fabs(value_max):
i_max = i
value_max = val
assert i_max is not None
assert value_max is not None
#
mpi_comm = vec.comm.tompi4py()
(global_value_max, global_i_max) = parallel_max(value_max, (i_max, ), fabs, mpi_comm)
return AbsOutput(global_value_max, global_i_max)
def _abs(matrix: Matrix.Type()):
# Note: PETSc offers a method MatGetRowMaxAbs, but it is not wrapped in petsc4py. We do the same by hand
mat = to_petsc4py(matrix)
row_start, row_end = mat.getOwnershipRange()
i_max, j_max = None, None
value_max = None
for i in range(row_start, row_end):
cols, vals = mat.getRow(i)
for (c, v) in zip(cols, vals):
if value_max is None or fabs(v) > fabs(value_max):
i_max = i
j_max = c
value_max = v
assert i_max is not None
assert j_max is not None
assert value_max is not None
#
mpi_comm = mat.comm.tompi4py()
(global_value_max, global_ij_max) = parallel_max(value_max, (i_max, j_max), fabs, mpi_comm)
return AbsOutput(global_value_max, global_ij_max)