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# Source code for networkx.generators.stochastic

    """Functions for generating stochastic graphs from a given weighted directed
    graph.

    """

    import networkx as nx
    from networkx.classes import DiGraph, MultiDiGraph
    from networkx.utils import not_implemented_for

    __all__ = ["stochastic_graph"]

    @not_implemented_for("undirected")
    @nx._dispatchable(
        edge_attrs="weight", mutates_input={"not copy": 1}, returns_graph=True
    )
    def stochastic_graph(G, copy=True, weight="weight"):
        """Returns a right-stochastic representation of directed graph `G`.

        A right-stochastic graph is a weighted digraph in which for each
        node, the sum of the weights of all the out-edges of that node is
        1. If the graph is already weighted (for example, via a 'weight'
        edge attribute), the reweighting takes that into account.

        Parameters
        ----------
        G : directed graph
            A :class:`~networkx.DiGraph` or :class:`~networkx.MultiDiGraph`.

        copy : boolean, optional
            If this is True, then this function returns a new graph with
            the stochastic reweighting. Otherwise, the original graph is
            modified in-place (and also returned, for convenience).

        weight : edge attribute key (optional, default='weight')
            Edge attribute key used for reading the existing weight and
            setting the new weight.  If no attribute with this key is found
            for an edge, then the edge weight is assumed to be 1. If an edge
            has a weight, it must be a positive number.

        """
        if copy:
            G = MultiDiGraph(G) if G.is_multigraph() else DiGraph(G)
        # There is a tradeoff here: the dictionary of node degrees may
        # require a lot of memory, whereas making a call to `G.out_degree`
        # inside the loop may be costly in computation time.
        degree = dict(G.out_degree(weight=weight))
        for u, v, d in G.edges(data=True):
            if degree[u] == 0:
                d[weight] = 0
            else:
                d[weight] = d.get(weight, 1) / degree[u]
        nx._clear_cache(G)
        return G