def nested_dissection(adjacency=None, xadj=None, adjncy=None):
"""This function computes fill reducing orderings of sparse matrices using
the multilevel nested dissection algorithm.
The input graph is given as either a Pythonic way as the `adjacency' parameter
or in the direct C-like way that Metis likes as `xadj' and `adjncy'. It
is an error to specify both graph inputs.
"""
xadj, adjncy = _prepare_graph(adjacency, xadj, adjncy)
from pymetis._internal import edge_nd
return edge_nd(xadj, adjncy)
def nested_dissection(adjacency=None, xadj=None, adjncy=None):
"""This function computes fill reducing orderings of sparse matrices using
the multilevel nested dissection algorithm.
The input graph is given as either a Pythonic way as the `adjacency' parameter
or in the direct C-like way that Metis likes as `xadj' and `adjncy'. It
is an error to specify both graph inputs.
"""
xadj, adncy = _prepare_graph(adjacency, xadj, adjncy)
from pymetis._internal import edge_nd
return edge_nd(xadj, adjncy)
def nd_ordering(g):
xadj = [0]
adjncy = []
adjacency = g.adjacency_list()
for i in range(len(adjacency)):
adj = adjacency[i]
if adj:
assert max(adj) < len(adjacency)
adjncy += map(int, adj)
xadj.append(len(adjncy))
(perm, iperm) = pymetis.edge_nd(xadj, adjncy)
return perm
def nd_ordering(g):
xadj = [0]
adjncy = []
adjacency = g.adjacency_list()
for i in range(len(adjacency)):
adj = adjacency[i]
if adj:
assert max(adj) < len(adjacency)
adjncy += map(int, adj)
xadj.append(len(adjncy))
(perm, iperm) = pymetis.edge_nd(xadj, adjncy)
return perm
