U td@s(ddlmZmZd ddZd ddZd S) )_graph_from_sparse_matrix"_graph_from_weighted_sparse_matrixdirectedc Osddlm}z ddl}Wntk r0d}YnXzddlm}Wntk rZd}YnXz ddl}Wntk rd}YnX|dk rt||jrt |||dS|dk rt||j r|j } |j }nd} |dk rt||jr| }t||j||d} | dk r| | jd<| S)aGenerates 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) - a pandas.DataFrame (column/row names must match, and will be used as vertex names). @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) rGraphNsparse)modename)igraphrnumpy ImportErrorscipyrpandas isinstancespmatrixr DataFrameindextolistvaluesndarraysuperZ Adjacencyvs) clsmatrixr argskwargsrnprpd vertex_namesgraphr!L/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/igraph/io/adjacency.py_construct_graph_from_adjacencys2         r#weightTc Cs*ddlm}z ddl}Wntk r0d}YnXzddlm}Wntk rZd}YnXz ddl}Wntk rd}YnX|dk rt||jrt |||||dS|dk rt||j r|j } |j }nd} |dk rt||jr| }t||j|||d\} } | | j|<| dk r&| | jd<| S)aFGenerates a graph from its weighted 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) These values can also be given as strings without the C{ADJ} prefix. @param attr: the name of the edge attribute that stores the edge weights. @param loops: whether to include loop edges. When C{False}, the diagonal of the adjacency matrix will be ignored. rrNr)r attrloops)r r&r )r rr r rrrrrrrrrrrrZ_Weighted_Adjacencyesr) rrr r%r&rrrrrr weightsr!r!r"(_construct_graph_from_weighted_adjacencyEsH            r)N)r)rr$T)Zigraph.sparse_matrixrrr#r)r!r!r!r"s  ?