"""Methods for converting to and from an incidence matrix."""
from scipy.sparse import coo_array
from ..exception import XGIError
from ..generators import empty_hypergraph
from ..linalg import incidence_matrix
__all__ = ["from_incidence_matrix", "to_incidence_matrix"]
[docs]def from_incidence_matrix(d, create_using=None, nodelabels=None, edgelabels=None):
"""Create a hypergraph from an incidence matrix
Parameters
----------
d : numpy array or a scipy sparse arrary
The incidence matrix where rows specify nodes and columns specify edges.
create_using : Hypergraph constructor, optional
The hypergraph object to add the data to, by default None
nodelabels : list or 1D numpy array, optional
The ordered list of node IDs to map the indices
of the incidence matrix to, by default None
edgelabels : list or 1D numpy array, optional
The ordered list of edge IDs to map the indices
of the incidence matrix to, by default None
Returns
-------
Hypergraph object
The constructed hypergraph
Raises
------
XGIError
Raises an error if the specified labels are the wrong dimensions
See Also
--------
incidence_matrix
to_incidence_matrix
"""
I = coo_array(d)
n, m = I.shape
if nodelabels is None:
nodedict = dict(zip(range(n), range(n)))
elif nodelabels is not None and len(nodelabels) != n:
raise XGIError("Node dictionary is the wrong size.")
else:
nodedict = dict(zip(range(n), nodelabels))
if edgelabels is None:
edgedict = dict(zip(range(m), range(m)))
elif edgelabels is not None and len(edgelabels) != m:
raise XGIError("Edge dictionary is the wrong size.")
else:
edgedict = dict(zip(range(m), edgelabels))
H = empty_hypergraph(create_using)
for node, edge in zip(I.row, I.col):
node = nodedict[node]
edge = edgedict[edge]
H.add_node_to_edge(edge, node)
return H
[docs]def to_incidence_matrix(H, sparse=True, index=False):
"""Convert a hypergraph to an incidence matrix.
Parameters
----------
H : Hypergraph object
The hypergraph of interest
sparse : bool, optional
Whether the constructed incidence matrix
should be sparse, by default True
index : bool, optional
Whether to return the corresponding
node and edge labels, by default False
Returns
-------
numpy.ndarray or scipy csr_array
The incidence matrix
dict
The dictionary mapping indices to node IDs, if index is True
dict
The dictionary mapping indices to edge IDs, if index is True
See Also
--------
incidence_matrix
from_incidence_matrix
"""
return incidence_matrix(H, sparse=sparse, index=index)