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This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

Source code for networkx.readwrite.json_graph.adjacency

#    Copyright (C) 2011-2013 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.
from itertools import chain
import networkx as nx
__author__ = """Aric Hagberg <aric.hagberg@gmail.com>"""
__all__ = ['adjacency_data', 'adjacency_graph']

_attrs = dict(id='id', key='key')


[docs]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(dict(chain(G.nodes[n].items(), [(id_, n)]))) adj = [] if multigraph: for nbr, keys in nbrdict.items(): for k, d in keys.items(): adj.append(dict(chain(d.items(), [(id_, nbr), (key, k)]))) else: for nbr, d in nbrdict.items(): adj.append(dict(chain(d.items(), [(id_, nbr)]))) data['adjacency'].append(adj) return data
[docs]def adjacency_graph(data, directed=False, multigraph=True, attrs=_attrs): """Returns graph from adjacency data format. Parameters ---------- data : dict Adjacency list formatted graph data Returns ------- G : NetworkX graph A NetworkX graph object 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')`. 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(tdata) else: ky = target_data.pop(key, None) graph.add_edge(source, target, key=ky) graph[source][target][ky].update(tdata) return graph