🔨 Export

Export Directed Acyclic Graph (DAG) to list, dictionary, and pandas DataFrame.

DAG Export Methods

Export DAG to	Method	Extract node attributes
List	`dag_to_list`	No
Dictionary	`dag_to_dict`	Yes with `attr_dict` or `all_attrs`
DataFrame	`dag_to_dataframe`	Yes with `attr_dict` or `all_attrs`
Dot (for .dot, .png, .svg, .jpeg, etc.)	`dag_to_dot`	No

bigtree.dag.export

dag_to_list

dag_to_list(dag)

Export DAG to list of tuples containing parent-child names.

Examples:

>>> from bigtree import DAGNode, DAG
>>> a = DAGNode("a", step=1)
>>> b = DAGNode("b", step=1)
>>> c = DAGNode("c", step=2, parents=[a, b])
>>> d = DAGNode("d", step=2, parents=[a, c])
>>> e = DAGNode("e", step=3, parents=[d])
>>> DAG(a).to_list()
[('a', 'c'), ('a', 'd'), ('b', 'c'), ('c', 'd'), ('d', 'e')]

Parameters:

Name	Type	Description	Default
`dag`	`T`	DAG to be exported	required

Returns:

Type	Description
`list[tuple[str, str]]`	List of tuples containing parent-child names

dag_to_dict

dag_to_dict(
    dag,
    parent_key="parents",
    attr_dict=None,
    all_attrs=False,
)

Export DAG to dictionary. Exported dictionary will have key as child name, and values as a dictionary of parent names and node attributes.

Examples:

>>> from bigtree import DAGNode, DAG
>>> a = DAGNode("a", step=1)
>>> b = DAGNode("b", step=1)
>>> c = DAGNode("c", step=2, parents=[a, b])
>>> d = DAGNode("d", step=2, parents=[a, c])
>>> e = DAGNode("e", step=3, parents=[d])
>>> DAG(a).to_dict(parent_key="parent", attr_dict={"step": "step no."})
{'a': {'step no.': 1}, 'c': {'parent': ['a', 'b'], 'step no.': 2}, 'd': {'parent': ['a', 'c'], 'step no.': 2}, 'b': {'step no.': 1}, 'e': {'parent': ['d'], 'step no.': 3}}

Parameters:

Name	Type	Description	Default
`dag`	`T`	DAG to be exported	required
`parent_key`	`str`	dictionary key for `node.parent.node_name`	`'parents'`
`attr_dict`	`Mapping[str, str] \| None`	node attributes mapped to dictionary key, key: node attributes, value: corresponding dictionary key	`None`
`all_attrs`	`bool`	indicator whether to retrieve all `Node` attributes	`False`

Returns:

Type	Description
`dict[str, Any]`	Dictionary of node names to their attributes

dag_to_dataframe

dag_to_dataframe(
    dag,
    name_col="name",
    parent_col="parent",
    attr_dict=None,
    all_attrs=False,
)

Export DAG to pandas DataFrame.

Examples:

>>> from bigtree import DAGNode, DAG
>>> a = DAGNode("a", step=1)
>>> b = DAGNode("b", step=1)
>>> c = DAGNode("c", step=2, parents=[a, b])
>>> d = DAGNode("d", step=2, parents=[a, c])
>>> e = DAGNode("e", step=3, parents=[d])
>>> DAG(a).to_dataframe(name_col="name", parent_col="parent", attr_dict={"step": "step no."})
  name parent  step no.
0    a   None         1
1    c      a         2
2    d      a         2
3    b   None         1
4    c      b         2
5    d      c         2
6    e      d         3

Parameters:

Name	Type	Description	Default
`dag`	`T`	DAG to be exported	required
`name_col`	`str`	column name for `node.node_name`	`'name'`
`parent_col`	`str`	column name for `node.parent.node_name`	`'parent'`
`attr_dict`	`Mapping[str, str] \| None`	node attributes mapped to column name, key: node attributes, value: corresponding column in dataframe	`None`
`all_attrs`	`bool`	indicator whether to retrieve all `Node` attributes	`False`

Returns:

Type	Description
`DataFrame`	pandas DataFrame of DAG information

dag_to_dot

dag_to_dot(
    dag,
    rankdir="TB",
    bg_colour=None,
    node_colour=None,
    node_shape=None,
    edge_colour=None,
    node_attr=None,
    edge_attr=None,
)

Export DAG or list of DAGs to image. Note that node names must be unique. Possible node attributes include style, fillcolor, or shape.

Examples:

>>> from bigtree import DAGNode, DAG
>>> a = DAGNode("a", step=1)
>>> b = DAGNode("b", step=1)
>>> c = DAGNode("c", step=2, parents=[a, b])
>>> d = DAGNode("d", step=2, parents=[a, c])
>>> e = DAGNode("e", step=3, parents=[d])
>>> dag_graph = DAG(a).to_dot()

Display image directly without saving (requires IPython)

>>> from IPython.display import Image, display
>>> plt = Image(dag_graph.create_png())
>>> display(plt)
<IPython.core.display.Image object>

Export to image, dot file, etc.

>>> dag_graph.write_png("assets/tree_dag.png")
>>> dag_graph.write_dot("assets/tree_dag.dot")

Export to string

>>> dag_graph.to_string()
'strict digraph G {\nrankdir=TB;\nc [label=c];\na [label=a];\na -> c;\nd [label=d];\na [label=a];\na -> d;\nc [label=c];\nb [label=b];\nb -> c;\nd [label=d];\nc [label=c];\nc -> d;\ne [label=e];\nd [label=d];\nd -> e;\n}\n'

Parameters:

Name	Type	Description	Default
`dag`	`T \| list[T]`	DAG(s) to be exported	required
`rankdir`	`str`	set direction of graph layout, accepts 'TB', 'BT, 'LR', or 'RL'	`'TB'`
`bg_colour`	`str \| None`	background color of image	`None`
`node_colour`	`str \| None`	fill colour of nodes	`None`
`node_shape`	`str \| None`	shape of nodes. Possible node_shape include "circle", "square", "diamond", "triangle"	`None`
`edge_colour`	`str \| None`	colour of edges	`None`
`node_attr`	`str \| None`	node attribute for style, overrides node_colour. Possible node attributes include {"style": "filled", "fillcolor": "gold"}	`None`
`edge_attr`	`str \| None`	edge attribute for style, overrides edge_colour. Possible edge attributes include {"style": "bold", "label": "edge label", "color": "black"}	`None`

Returns:

Type	Description
`Dot`	pydot object of DAG