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# Source code for networkx.readwrite.json_graph.adjacency

    import networkx as nx

    __all__ = ["adjacency_data", "adjacency_graph"]

    _attrs = {"id": "id", "key": "key"}

    def adjacency_data(G, attrs=_attrs):
        """Returns data in adjacency format that is suitable for JSON serialization
        and use in JavaScript documents.

        Parameters
        ----------
        G : NetworkX graph

        attrs : dict
            A dictionary that contains two keys 'id' and 'key'. The corresponding
            values provide the attribute names for storing NetworkX-internal graph
            data. The values should be unique. Default value:
            :samp:`dict(id='id', key='key')`.

            If some user-defined graph data use these attribute names as data keys,
            they may be silently dropped.

        Returns
        -------
        data : dict
           A dictionary with adjacency formatted data.

        Raises
        ------
        NetworkXError
            If values in attrs are not unique.

        Examples
        --------
        >>> from networkx.readwrite import json_graph
        >>> G = nx.Graph([(1, 2)])
        >>> data = json_graph.adjacency_data(G)

        To serialize with json

        >>> import json
        >>> s = json.dumps(data)

        Notes
        -----
        Graph, node, and link attributes will be written when using this format
        but attribute keys must be strings if you want to serialize the resulting
        data with JSON.

        The default value of attrs will be changed in a future release of NetworkX.

        See Also
        --------
        adjacency_graph, node_link_data, tree_data
        """
        multigraph = G.is_multigraph()
        id_ = attrs["id"]
        # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
        key = None if not multigraph else attrs["key"]
        if id_ == key:
            raise nx.NetworkXError("Attribute names are not unique.")
        data = {}
        data["directed"] = G.is_directed()
        data["multigraph"] = multigraph
        data["graph"] = list(G.graph.items())
        data["nodes"] = []
        data["adjacency"] = []
        for n, nbrdict in G.adjacency():
            data["nodes"].append({**G.nodes[n], id_: n})
            adj = []
            if multigraph:
                for nbr, keys in nbrdict.items():
                    for k, d in keys.items():
                        adj.append({**d, id_: nbr, key: k})
            else:
                for nbr, d in nbrdict.items():
                    adj.append({**d, id_: nbr})
            data["adjacency"].append(adj)
        return data

    @nx._dispatchable(graphs=None, returns_graph=True)
    def adjacency_graph(data, directed=False, multigraph=True, attrs=_attrs):
        """Returns graph from adjacency data format.

        Parameters
        ----------
        data : dict
            Adjacency list formatted graph data

        directed : bool
            If True, and direction not specified in data, return a directed graph.

        multigraph : bool
            If True, and multigraph not specified in data, return a multigraph.

        attrs : dict
            A dictionary that contains two keys 'id' and 'key'. The corresponding
            values provide the attribute names for storing NetworkX-internal graph
            data. The values should be unique. Default value:
            :samp:`dict(id='id', key='key')`.

        Returns
        -------
        G : NetworkX graph
           A NetworkX graph object

        Examples
        --------
        >>> from networkx.readwrite import json_graph
        >>> G = nx.Graph([(1, 2)])
        >>> data = json_graph.adjacency_data(G)
        >>> H = json_graph.adjacency_graph(data)

        Notes
        -----
        The default value of attrs will be changed in a future release of NetworkX.

        See Also
        --------
        adjacency_graph, node_link_data, tree_data
        """
        multigraph = data.get("multigraph", multigraph)
        directed = data.get("directed", directed)
        if multigraph:
            graph = nx.MultiGraph()
        else:
            graph = nx.Graph()
        if directed:
            graph = graph.to_directed()
        id_ = attrs["id"]
        # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
        key = None if not multigraph else attrs["key"]
        graph.graph = dict(data.get("graph", []))
        mapping = []
        for d in data["nodes"]:
            node_data = d.copy()
            node = node_data.pop(id_)
            mapping.append(node)
            graph.add_node(node)
            graph.nodes[node].update(node_data)
        for i, d in enumerate(data["adjacency"]):
            source = mapping[i]
            for tdata in d:
                target_data = tdata.copy()
                target = target_data.pop(id_)
                if not multigraph:
                    graph.add_edge(source, target)
                    graph[source][target].update(target_data)
                else:
                    ky = target_data.pop(key, None)
                    graph.add_edge(source, target, key=ky)
                    graph[source][target][ky].update(target_data)
        return graph