(cls, matrix, mode="directed", *args, **kwargs)
return super().Adjacency(matrix, mode=mode)
Generates a graph from its adjacency matrix.

        @param matrix: the adjacency matrix. Possible types are:
          - a list of lists
          - a numpy 2D array or matrix (will be converted to list of lists)
          - a scipy.sparse matrix (will be converted to a COO matrix, but not
            to a dense matrix)
        @param mode: the mode to be used. Possible values are:
          - C{"directed"} - the graph will be directed and a matrix
            element gives the number of edges between two vertex.
          - C{"undirected"} - alias to C{"max"} for convenience.
          - C{"max"} - undirected graph will be created and the number of
            edges between vertex M{i} and M{j} is M{max(A(i,j), A(j,i))}
          - C{"min"} - like C{"max"}, but with M{min(A(i,j), A(j,i))}
          - C{"plus"}  - like C{"max"}, but with M{A(i,j) + A(j,i)}
          - C{"upper"} - undirected graph with the upper right triangle of
            the matrix (including the diagonal)
          - C{"lower"} - undirected graph with the lower left triangle of
            the matrix (including the diagonal)
Generates a graph from its adjacency matrix.
Generates a graph from its adjacency matrix .
def Adjacency(cls, matrix, mode="directed", *args, **kwargs):
        """Generates a graph from its adjacency matrix.

        @param matrix: the adjacency matrix. Possible types are:
          - a list of lists
          - a numpy 2D array or matrix (will be converted to list of lists)
          - a scipy.sparse matrix (will be converted to a COO matrix, but not
            to a dense matrix)
        @param mode: the mode to be used. Possible values are:
          - C{"directed"} - the graph will be directed and a matrix
            element gives the number of edges between two vertex.
          - C{"undirected"} - alias to C{"max"} for convenience.
          - C{"max"} - undirected graph will be created and the number of
            edges between vertex M{i} and M{j} is M{max(A(i,j), A(j,i))}
          - C{"min"} - like C{"max"}, but with M{min(A(i,j), A(j,i))}
          - C{"plus"}  - like C{"max"}, but with M{A(i,j) + A(j,i)}
          - C{"upper"} - undirected graph with the upper right triangle of
            the matrix (including the diagonal)
          - C{"lower"} - undirected graph with the lower left triangle of
            the matrix (including the diagonal)
        """
        try:
            import numpy as np
        except ImportError:
            np = None

        try:
            from scipy import sparse
        except ImportError:
            sparse = None

        if (sparse is not None) and isinstance(matrix, sparse.spmatrix):
            return _graph_from_sparse_matrix(cls, matrix, mode=mode)

        if (np is not None) and isinstance(matrix, np.ndarray):
            matrix = matrix.tolist()

        return super().Adjacency(matrix, mode=mode)
def Adjacency ( cls , matrix , mode = "directed" , * args , * * kwargs ) : try : import numpy as np except ImportError : np = None try : from scipy import sparse except ImportError : sparse = None if ( sparse is not None ) and isinstance ( matrix , sparse . spmatrix ) : return _graph_from_sparse_matrix ( cls , matrix , mode = mode ) if ( np is not None ) and isinstance ( matrix , np . ndarray ) : matrix = matrix . tolist ( ) return super ( ) . Adjacency ( matrix , mode = mode )