treevalue.utils.tree

build_graph

treevalue.utils.tree.build_graph(*roots, node_id_gen: Optional[Callable] = None, graph_title: Optional[str] = None, graph_name: Optional[str] = None, graph_cfg: Optional[Mapping[str, Any]] = None, repr_gen: Optional[Callable] = None, iter_gen: Optional[Callable] = None, node_cfg_gen: Optional[Callable] = None, edge_cfg_gen: Optional[Callable] = None) → graphviz.graphs.Digraph[source]
Overview:

Build a graphviz graph based on given tree structure.

Arguments:

  • roots: Root nodes of the graph.

  • node_id_gen (Optional[Callable]): Node id generation function, default is None which means based on object id.

  • graph_title (Optional[str]): Title of the graph, default is None which means generate automatically based on timestamp.

  • graph_name (Optional[str]): Name of the graph, default is None which means auto generated based on graph title.

  • graph_cfg (Optional[Mapping[str, Any]]): Configuration of graph, default is None which means no configuration.

  • repr_gen (Optional[Callable]): Representation format generator, default is None which means using repr function.

  • iter_gen (Optional[Callable]): Iterator generator, default is None which means load from items method.

  • node_cfg_gen (Optional[Callable]): Node configuration generator, default is None which means no configuration.

  • edge_cfg_gen (Optional[Callable]): Edge configuration generator, default is None which means no configuration.

Returns:

  • dot (Digraph): Graphviz directed graph object.

Here is an example of build_graph function. The source code is

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from treevalue.utils import build_graph

if __name__ == '__main__':
    t = {'a': 1, 'b': 2, 'x': {'c': 3, 'd': 4}}
    g = build_graph((t, 't'), graph_title="Demo of build_graph.")

    print(g.source)
    print(g.render('build_graph_demo.dat.gv', format='svg'))

The generated graphviz source code should be

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// Demo of build_graph.
digraph demo_of_build_graph {
	graph [label="Demo of build_graph."]
	node_7f65bb5b5280 [label=t]
	node_7f65bb5b5280__a [label=1]
	node_7f65bb5b5280 -> node_7f65bb5b5280__a [label=a]
	node_7f65bb5b5280__b [label=2]
	node_7f65bb5b5280 -> node_7f65bb5b5280__b [label=b]
	node_7f65bb5b5240 [label="t.x"]
	node_7f65bb5b5280 -> node_7f65bb5b5240 [label=x]
	node_7f65bb5b5240__c [label=3]
	node_7f65bb5b5240 -> node_7f65bb5b5240__c [label=c]
	node_7f65bb5b5240__d [label=4]
	node_7f65bb5b5240 -> node_7f65bb5b5240__d [label=d]
}

The graph should be

../../_images/build_graph_demo.dat.gv.svg

Also, multiple rooted graph is supported, this function will detect the pointer of the objects. Just like another complex source code below.

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from treevalue.utils import build_graph

if __name__ == '__main__':
    t1 = {'a': 1, 'b': 2, 'x': {'c': 3, 'd': 4}}
    t2 = {'f': 4, 'y': t1['x'], 'z': {'e': [5, 7], 'f': "string"}}
    g = build_graph((t1, 't1'), (t2, 't2'), graph_title="Complex demo of build_graph.")

    print(g.source)
    print(g.render('build_graph_complex_demo.dat.gv', format='svg'))

The exported graph should be

../../_images/build_graph_complex_demo.dat.gv.svg

The return value’s type of function graphics is class graphviz.dot.Digraph, from the opensource library graphviz, for further information of this project and graphviz.dot.Digraph’s usage, take a look at: