163 lines
4.9 KiB
Python
163 lines
4.9 KiB
Python
# Copyright (C) 2011-2013 by
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# Aric Hagberg <hagberg@lanl.gov>
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# Dan Schult <dschult@colgate.edu>
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# Pieter Swart <swart@lanl.gov>
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# All rights reserved.
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# BSD license.
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from itertools import chain
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import networkx as nx
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__author__ = """Aric Hagberg <aric.hagberg@gmail.com>"""
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__all__ = ['adjacency_data', 'adjacency_graph']
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_attrs = dict(id='id', key='key')
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def adjacency_data(G, attrs=_attrs):
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"""Returns data in adjacency format that is suitable for JSON serialization
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and use in Javascript documents.
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Parameters
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----------
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G : NetworkX graph
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attrs : dict
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A dictionary that contains two keys 'id' and 'key'. The corresponding
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values provide the attribute names for storing NetworkX-internal graph
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data. The values should be unique. Default value:
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:samp:`dict(id='id', key='key')`.
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If some user-defined graph data use these attribute names as data keys,
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they may be silently dropped.
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Returns
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-------
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data : dict
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A dictionary with adjacency formatted data.
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Raises
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------
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NetworkXError
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If values in attrs are not unique.
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Examples
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--------
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>>> from networkx.readwrite import json_graph
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>>> G = nx.Graph([(1,2)])
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>>> data = json_graph.adjacency_data(G)
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To serialize with json
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>>> import json
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>>> s = json.dumps(data)
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Notes
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-----
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Graph, node, and link attributes will be written when using this format
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but attribute keys must be strings if you want to serialize the resulting
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data with JSON.
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The default value of attrs will be changed in a future release of NetworkX.
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See Also
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--------
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adjacency_graph, node_link_data, tree_data
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"""
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multigraph = G.is_multigraph()
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id_ = attrs['id']
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# Allow 'key' to be omitted from attrs if the graph is not a multigraph.
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key = None if not multigraph else attrs['key']
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if id_ == key:
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raise nx.NetworkXError('Attribute names are not unique.')
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data = {}
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data['directed'] = G.is_directed()
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data['multigraph'] = multigraph
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data['graph'] = list(G.graph.items())
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data['nodes'] = []
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data['adjacency'] = []
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for n, nbrdict in G.adjacency():
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data['nodes'].append(dict(chain(G.nodes[n].items(), [(id_, n)])))
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adj = []
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if multigraph:
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for nbr, keys in nbrdict.items():
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for k, d in keys.items():
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adj.append(dict(chain(d.items(), [(id_, nbr), (key, k)])))
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else:
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for nbr, d in nbrdict.items():
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adj.append(dict(chain(d.items(), [(id_, nbr)])))
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data['adjacency'].append(adj)
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return data
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def adjacency_graph(data, directed=False, multigraph=True, attrs=_attrs):
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"""Returns graph from adjacency data format.
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Parameters
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----------
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data : dict
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Adjacency list formatted graph data
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Returns
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-------
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G : NetworkX graph
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A NetworkX graph object
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directed : bool
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If True, and direction not specified in data, return a directed graph.
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multigraph : bool
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If True, and multigraph not specified in data, return a multigraph.
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attrs : dict
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A dictionary that contains two keys 'id' and 'key'. The corresponding
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values provide the attribute names for storing NetworkX-internal graph
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data. The values should be unique. Default value:
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:samp:`dict(id='id', key='key')`.
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Examples
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--------
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>>> from networkx.readwrite import json_graph
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>>> G = nx.Graph([(1,2)])
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>>> data = json_graph.adjacency_data(G)
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>>> H = json_graph.adjacency_graph(data)
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Notes
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-----
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The default value of attrs will be changed in a future release of NetworkX.
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See Also
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--------
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adjacency_graph, node_link_data, tree_data
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"""
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multigraph = data.get('multigraph', multigraph)
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directed = data.get('directed', directed)
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if multigraph:
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graph = nx.MultiGraph()
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else:
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graph = nx.Graph()
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if directed:
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graph = graph.to_directed()
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id_ = attrs['id']
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# Allow 'key' to be omitted from attrs if the graph is not a multigraph.
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key = None if not multigraph else attrs['key']
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graph.graph = dict(data.get('graph', []))
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mapping = []
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for d in data['nodes']:
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node_data = d.copy()
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node = node_data.pop(id_)
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mapping.append(node)
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graph.add_node(node)
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graph.nodes[node].update(node_data)
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for i, d in enumerate(data['adjacency']):
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source = mapping[i]
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for tdata in d:
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target_data = tdata.copy()
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target = target_data.pop(id_)
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if not multigraph:
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graph.add_edge(source, target)
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graph[source][target].update(tdata)
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else:
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ky = target_data.pop(key, None)
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graph.add_edge(source, target, key=ky)
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graph[source][target][ky].update(tdata)
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return graph
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