from itertools import chain, count
import networkx as nx
__all__ = ["node_link_data", "node_link_graph"]
_attrs = dict(source="source", target="target", name="id", key="key", link="links")
def _to_tuple(x):
"""Converts lists to tuples, including nested lists.
All other non-list inputs are passed through unmodified. This function is
intended to be used to convert potentially nested lists from json files
into valid nodes.
Examples
--------
>>> _to_tuple([1, 2, [3, 4]])
(1, 2, (3, 4))
"""
if not isinstance(x, (tuple, list)):
return x
return tuple(map(_to_tuple, x))
[文档]def node_link_data(G, attrs=None):
"""Returns data in node-link format that is suitable for JSON serialization
and use in Javascript documents.
Parameters
----------
G : NetworkX graph
attrs : dict
A dictionary that contains five keys 'source', 'target', 'name',
'key' and 'link'. The corresponding values provide the attribute
names for storing NetworkX-internal graph data. The values should
be unique. Default value::
dict(source='source', target='target', name='id',
key='key', link='links')
If some user-defined graph data use these attribute names as data keys,
they may be silently dropped.
Returns
-------
data : dict
A dictionary with node-link formatted data.
Raises
------
NetworkXError
If values in attrs are not unique.
Examples
--------
>>> from networkx.readwrite import json_graph
>>> G = nx.Graph([("A", "B")])
>>> data1 = json_graph.node_link_data(G)
>>> H = nx.gn_graph(2)
>>> data2 = json_graph.node_link_data(
... H, {"link": "edges", "source": "from", "target": "to"}
... )
To serialize with json
>>> import json
>>> s1 = json.dumps(data1)
>>> s2 = json.dumps(
... data2, default={"link": "edges", "source": "from", "target": "to"}
... )
Notes
-----
Graph, node, and link attributes are stored in this format. Note that
attribute keys will be converted to strings in order to comply with JSON.
Attribute 'key' is only used for multigraphs.
See Also
--------
node_link_graph, adjacency_data, tree_data
"""
multigraph = G.is_multigraph()
# Allow 'attrs' to keep default values.
if attrs is None:
attrs = _attrs
else:
attrs.update({k: v for (k, v) in _attrs.items() if k not in attrs})
name = attrs["name"]
source = attrs["source"]
target = attrs["target"]
links = attrs["link"]
# Allow 'key' to be omitted from attrs if the graph is not a multigraph.
key = None if not multigraph else attrs["key"]
if len({source, target, key}) < 3:
raise nx.NetworkXError("Attribute names are not unique.")
data = {
"directed": G.is_directed(),
"multigraph": multigraph,
"graph": G.graph,
"nodes": [dict(chain(G.nodes[n].items(), [(name, n)])) for n in G],
}
if multigraph:
data[links] = [
dict(chain(d.items(), [(source, u), (target, v), (key, k)]))
for u, v, k, d in G.edges(keys=True, data=True)
]
else:
data[links] = [
dict(chain(d.items(), [(source, u), (target, v)]))
for u, v, d in G.edges(data=True)
]
return data
[文档]def node_link_graph(data, directed=False, multigraph=True, attrs=None):
"""Returns graph from node-link data format.
Parameters
----------
data : dict
node-link 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 five keys 'source', 'target', 'name',
'key' and 'link'. The corresponding values provide the attribute
names for storing NetworkX-internal graph data. Default value:
dict(source='source', target='target', name='id',
key='key', link='links')
Returns
-------
G : NetworkX graph
A NetworkX graph object
Examples
--------
>>> from networkx.readwrite import json_graph
>>> G = nx.Graph([("A", "B")])
>>> data = json_graph.node_link_data(G)
>>> H = json_graph.node_link_graph(data)
Notes
-----
Attribute 'key' is only used for multigraphs.
See Also
--------
node_link_data, adjacency_data, tree_data
"""
# Allow 'attrs' to keep default values.
if attrs is None:
attrs = _attrs
else:
attrs.update({k: v for k, v in _attrs.items() if k not in attrs})
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()
name = attrs["name"]
source = attrs["source"]
target = attrs["target"]
links = attrs["link"]
# Allow 'key' to be omitted from attrs if the graph is not a multigraph.
key = None if not multigraph else attrs["key"]
graph.graph = data.get("graph", {})
c = count()
for d in data["nodes"]:
node = _to_tuple(d.get(name, next(c)))
nodedata = {str(k): v for k, v in d.items() if k != name}
graph.add_node(node, **nodedata)
for d in data[links]:
src = tuple(d[source]) if isinstance(d[source], list) else d[source]
tgt = tuple(d[target]) if isinstance(d[target], list) else d[target]
if not multigraph:
edgedata = {str(k): v for k, v in d.items() if k != source and k != target}
graph.add_edge(src, tgt, **edgedata)
else:
ky = d.get(key, None)
edgedata = {
str(k): v
for k, v in d.items()
if k != source and k != target and k != key
}
graph.add_edge(src, tgt, ky, **edgedata)
return graph