module documentation
Implementation of Python-level sparse matrix operations.
Function | _convert |
Undocumented |
Function | _graph |
Construct graph from sparse matrix, unweighted. |
Function | _graph |
Construct graph from sparse matrix, weighted |
Function | _maybe |
Halves all items in the diagonal of the given SciPy sparse matrix in coo mode if and only if the given condition is True. |
Constant | _SUPPORTED |
Undocumented |
Construct graph from sparse matrix, unweighted.
@param loops: specifies how the diagonal of the matrix should be handled:
- C{"ignore"} - ignore loop edges in the diagonal
- C{"once"} - treat the diagonal entries as loop edge counts
- C{"twice"} - treat the diagonal entries as I{twice} the number of loop edges
def _graph_from_weighted_sparse_matrix(klass, matrix, mode=ADJ_DIRECTED, attr='weight', loops='once'):
(source)
¶
Construct graph from sparse matrix, weighted
NOTE: Of course, you cannot emcompass a fully general weighted multigraph with a single adjacency matrix, so we don't try to do it here either.
- @param loops: specifies how to handle loop edges. When C{False} or
- C{"ignore"}, the diagonal of the adjacency matrix will be ignored. When C{True} or C{"once"}, the diagonal is assumed to contain the weight of the corresponding loop edge. When C{"twice"}, the diagonal is assumed to contain I{twice} the weight of the corresponding loop edge.