get_hierarchy_control_points

graph_tool.draw.get_hierarchy_control_points(g, t, tpos, beta=0.8, cts=None, is_tree=True, max_depth=None)

Return the Bézier spline control points for the edges in g, given the hierarchical structure encoded in graph t.

Parameters:

gGraph

Graph to be drawn.

tGraph

Directed graph containing the hierarchy of g. It must be a directed tree with a single root. The direction of the edges point from the root to the leaves, and the vertices in t with index in the range \([0, N-1]\), with \(N\) being the number of vertices in g, must correspond to the respective vertex in g.

tposVertexPropertyMap

Vector-valued vertex property map containing the x and y coordinates of the vertices in graph t.

betafloat (optional, default: 0.8 or EdgePropertyMap)

Edge bundling strength. For beta == 0 the edges are straight lines, and for beta == 1 they strictly follow the hierarchy. This can be optionally an edge property map, which specified a different bundling strength for each edge.

ctsEdgePropertyMap (optional, default: None)

Edge property map of type vector<double> where the control points will be stored.

is_treebool (optional, default: True)

If True, t must be a directed tree, otherwise it can be any connected graph.

max_depthint (optional, default: None)

If supplied, only the first max_depth bottom levels of the hierarchy will be used.

Returns:

ctsEdgePropertyMap

Vector-valued edge property map containing the Bézier spline control points for the edges in g.

Notes

This is an implementation of the edge-bundling algorithm described in [holten-hierarchical-2006].

References

[holten-hierarchical-2006]

Holten, D. “Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data.”, IEEE Transactions on Visualization and Computer Graphics 12, no. 5, 741–748 (2006). DOI: 10.1109/TVCG.2006.147 [sci-hub, @tor]

Examples

>>> g = gt.collection.data["netscience"]
>>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
>>> state = gt.minimize_nested_blockmodel_dl(g)
>>> t = gt.get_hierarchy_tree(state)[0]
>>> tpos = pos = gt.radial_tree_layout(t, t.vertex(t.num_vertices() - 1, use_index=False), weighted=True)
>>> cts = gt.get_hierarchy_control_points(g, t, tpos)
>>> pos = g.own_property(tpos)
>>> b = state.levels[0].b
>>> shape = b.copy()
>>> shape.a %= 14
>>> gt.graph_draw(g, pos=pos, vertex_fill_color=b, vertex_shape=shape, edge_control_points=cts,
...               edge_color=[0, 0, 0, 0.3], vertex_anchor=0, output="netscience_nested_mdl.pdf")
<...>

Block partition of a co-authorship network, which minimizes the description length of the network according to the nested (degree-corrected) stochastic blockmodel.