Function to_numpy_array

python/graphscope/nx/convert_matrix.py:96–182

(
    G,
    nodelist=None,
    dtype=None,
    order=None,
    multigraph_weight=sum,
    weight="weight",
    nonedge=0.0,
)

def to_numpy_array(
    G,
    nodelist=None,
    dtype=None,
    order=None,
    multigraph_weight=sum,
    weight="weight",
    nonedge=0.0,
):
    import numpy as np

    if nodelist is None:
        nodelist = list(G)
        nodeset = G
        nlen = len(G)
    else:
        nlen = len(nodelist)
        nodeset = set(G.nbunch_iter(nodelist))
        if nlen != len(nodeset):
            for n in nodelist:
                if n not in G:
                    raise nx.NetworkXError(f"Node {n} in nodelist is not in G")
            raise nx.NetworkXError("nodelist contains duplicates.")

    A = np.full((nlen, nlen), fill_value=nonedge, dtype=dtype, order=order)

    # Corner cases: empty nodelist or graph without any edges
    if nlen == 0 or G.number_of_edges() == 0:
        return A

    # If dtype is structured and weight is None, use dtype field names as
    # edge attributes
    edge_attrs = None  # Only single edge attribute by default
    if A.dtype.names:
        if weight is None:
            edge_attrs = dtype.names
        else:
            raise ValueError(
                "Specifying `weight` not supported for structured dtypes\n."
                "To create adjacency matrices from structured dtypes, use `weight=None`."
            )

    idx = dict(zip(sorted(nodelist), range(nlen)))
    if len(nodelist) < len(G):
        G = G.subgraph(nodelist)  # A real subgraph, not view

    # Collect all edge weights and reduce with `multigraph_weights`
    if G.is_multigraph():
        if edge_attrs:
            raise nx.NetworkXError(
                "Structured arrays are not supported for MultiGraphs"
            )
        d = defaultdict(list)
        for u, v, wt in G.edges(data=weight, default=1.0):
            d[(idx[u], idx[v])].append(wt)
        i, j = np.array(list(d.keys())).T  # indices
        wts = [multigraph_weight(ws) for ws in d.values()]  # reduced weights
    else: