graph_tool.draw - Graph drawing

Summary

graph_draw (g[, pos, size, 15), ...])	Draw a graph using graphviz.
arf_layout (g[, weight, d, a, dt, ...])	Calculate the ARF spring-block layout of the graph.
random_layout (g[, shape, pos, dim])	Performs a random layout of the graph.

Contents

graph_tool.draw.graph_draw(g, pos=None, size=(15, 15), pin=False, layout='neato', maxiter=None, ratio='fill', overlap='prism', sep=None, splines=False, vsize=0.10000000000000001, penwidth=1.0, elen=None, gprops={}, vprops={}, eprops={}, vcolor=None, ecolor=None, vcmap=<matplotlib.colors.LinearSegmentedColormap instance at 0x3e54998>, vnorm=True, ecmap=<matplotlib.colors.LinearSegmentedColormap instance at 0x3e54998>, enorm=True, output='', output_format='auto', returngv=False, fork=False, return_bitmap=False, seed=0)

Draw a graph using graphviz.

Parameters:

g : Graph

Graph to be used.

pos : PropertyMap or tuple of PropertyMaps (optional, default: None)

Vertex property maps containing the x and y coordinates of the vertices.

size : tuple of scalars (optional, default: (15,15))

Size (in centimeters) of the canvas.

pin : bool (default: False)

If True, the vertices are not moved from their initial position.

layout : string (default: “neato”)

Layout engine to be used. Possible values are “neato”, “fdp”, “dot”, “circo”, “twopi” and “arf”.

maxiter : int (default: None)

If specified, limits the maximum number of iterations.

ratio : string or float (default: “fill”)

Sets the aspect ratio (drawing height/drawing width) for the drawing. Note that this is adjusted before the ‘size’ attribute constraints are enforced.

If ratio is numeric, it is taken as the desired aspect ratio. Then, if the actual aspect ratio is less than the desired ratio, the drawing height is scaled up to achieve the desired ratio; if the actual ratio is greater than that desired ratio, the drawing width is scaled up.

If ratio = “fill” and the size attribute is set, node positions are scaled, separately in both x and y, so that the final drawing exactly fills the specified size.

If ratio = “compress” and the size attribute is set, dot attempts to compress the initial layout to fit in the given size. This achieves a tighter packing of nodes but reduces the balance and symmetry. This feature only works in dot.

If ratio = “expand”, the size attribute is set, and both the width and the height of the graph are less than the value in size, node positions are scaled uniformly until at least one dimension fits size exactly. Note that this is distinct from using size as the desired size, as here the drawing is expanded before edges are generated and all node and text sizes remain unchanged.

If ratio = “auto”, the page attribute is set and the graph cannot be drawn on a single page, then size is set to an “ideal” value. In particular, the size in a given dimension will be the smallest integral multiple of the page size in that dimension which is at least half the current size. The two dimensions are then scaled independently to the new size. This feature only works in dot.

overlap : bool or string (default: “prism”)

Determines if and how node overlaps should be removed. Nodes are first enlarged using the sep attribute. If True, overlaps are retained. If the value is “scale”, overlaps are removed by uniformly scaling in x and y. If the value is False, node overlaps are removed by a Voronoi-based technique. If the value is “scalexy”, x and y are separately scaled to remove overlaps.

If sfdp is available, one can set overlap to “prism” to use a proximity graph-based algorithm for overlap removal. This is the preferred technique, though “scale” and False can work well with small graphs. This technique starts with a small scaling up, controlled by the overlap_scaling attribute, which can remove a significant portion of the overlap. The prism option also accepts an optional non-negative integer suffix. This can be used to control the number of attempts made at overlap removal. By default, overlap=”prism” is equivalent to overlap=”prism1000”. Setting overlap=”prism0” causes only the scaling phase to be run.

If the value is “compress”, the layout will be scaled down as much as possible without introducing any overlaps, obviously assuming there are none to begin with.

sep : float (default: None)

Specifies margin to leave around nodes when removing node overlap. This guarantees a minimal non-zero distance between nodes.

splines : bool (default: False)

If True, the edges are drawn as splines and routed around the vertices.

vsize : float, PropertyMap, or tuple (default: 0.1)

Default vertex size (width and height). If a tuple is specified, the first value should be a property map, and the second is a scale factor.

penwidth : float, PropertyMap or tuple (default: 1.0)

Specifies the width of the pen, in points, used to draw lines and curves, including the boundaries of edges and clusters. It has no effect on text. If a tuple is specified, the first value should be a property map, and the second is a scale factor.

elen : float or PropertyMap (default: None)

Preferred edge length, in inches.

gprops : dict (default: {})

Additional graph properties, as a dictionary. The keys are the property names, and the values must be convertible to string.

vprops : dict (default: {})

Additional vertex properties, as a dictionary. The keys are the property names, and the values must be convertible to string, or vertex property maps, with values convertible to strings.

eprops : dict (default: {})

Additional edge properties, as a dictionary. The keys are the property names, and the values must be convertible to string, or edge property maps, with values convertible to strings.

vcolor : string or PropertyMap (default: None)

Drawing color for vertices. If the valued supplied is a property map, the values must be scalar types, whose color values are obtained from the ‘vcmap’ argument.

ecolor : string or PropertyMap (default: None)

Drawing color for edges. If the valued supplied is a property map, the values must be scalar types, whose color values are obtained from the ‘ecmap’ argument.

vcmap : matplotlib.colors.Colormap (default: matplotlib.cm.jet)

Vertex color map.

vnorm : bool (default: True)

Normalize vertex color values to the [0,1] range.

ecmap : matplotlib.colors.Colormap (default: matplotlib.cm.jet)

Edge color map.

enorm : bool (default: True)

Normalize edge color values to the [0,1] range.

output : string (default: “”)

Output file name.

output_format : string (default: “auto”)

Output file format. Possible values are “auto”, “xlib”, “ps”, “svg”, “svgz”, “fig”, “mif”, “hpgl”, “pcl”, “png”, “gif”, “dia”, “imap”, “cmapx”. If the value is “auto”, the format is guessed from the ‘output’ parameter, or ‘xlib’ if it is empty. If the value is None, no output is produced.

returngv : bool (default: False)

Return the graph object used internally with the gv module.

fork : bool (default: False)

If True, the program is forked before drawing. This is used as a work-around for a bug in graphviz, where the exit() function is called, which would cause the calling program to end. This is always assumed ‘True’, if output_format = ‘xlib’.

return_bitmap : bool (default: False)

If True, a bitmap (ndarray) of the rendered graph is returned.

Returns:

pos : PropertyMap

Vector vertex property map with the x and y coordinates of the vertices.

gv : gv.digraph or gv.graph (optional, only if returngv == True)

Internally used graphviz graph.

Notes

This function is a wrapper for the [graphviz] python routines. Extensive additional documentation for the graph, vertex and edge properties is available at: http://www.graphviz.org/doc/info/attrs.html.

References

[graphviz]

http://www.graphviz.org

Examples

>>> from numpy import *
>>> from numpy.random import seed, zipf
>>> seed(42)
>>> g = gt.random_graph(1000, lambda: min(zipf(2.4), 40),
...                     lambda i,j: exp(abs(i-j)), directed=False)
>>> # extract largest component
>>> comp = gt.label_components(g)
>>> h = gt.vertex_hist(g, comp)
>>> max_comp = h[1][list(h[0]).index(max(h[0]))]
>>> g.remove_vertex_if(lambda v: comp[v] != max_comp)
>>>
>>> deg = g.degree_property_map("out")
>>> deg.get_array()[:] = 2*(sqrt(deg.get_array()[:])*0.5 + 0.4)
>>> ebet = gt.betweenness(g)[1]
>>> ebet.get_array()[:] *= 4000
>>> ebet.get_array()[:] += 10
>>> gt.graph_draw(g, vsize=deg, vcolor=deg, elen=10, ecolor=ebet,
...               penwidth=ebet, overlap="prism", output="graph-draw.png")
<...>

Kamada-Kawai force-directed layout of a graph with a power-law degree distribution, and dissortative degree correlation. The vertex size and color indicate the degree, and the edge color and width the edge betweeness centrality.

graph_tool.draw.arf_layout(g, weight=None, d=0.10000000000000001, a=10, dt=0.001, epsilon=9.9999999999999995e-07, max_iter=1000, pos=None, dim=2)

Calculate the ARF spring-block layout of the graph.

Parameters:

g : Graph

Graph to be used.

weight : PropertyMap (optional, default: None)

An edge property map with the respective weights.

d : float (optional, default: 0.1)

Opposing force between vertices.

a : float (optional, default: 10)

Attracting force between adjacent vertices.

dt : float (optional, default: 0.001)

Iteration step size.

epsilon : float (optional, default: 1e-6)

Convergence criterion.

max_iter : int (optional, default: 1000)

Maximum number of iterations. If this value is 0, it runs until convergence.

pos : PropertyMap (optional, default: None)

Vector vertex property maps where the coordinates should be stored.

dim : int (optional, default: 2)

Number of coordinates per vertex.

Returns:

pos : A vector vertex property map

Vertex property map with the coordinates of the vertices.

Notes

This algorithm is defined in [geipel-self-organization-2007], and has complexity O(V^2).

References

[geipel-self-organization-2007]

Markus M. Geipel, “Self-Organization applied to Dynamic Network Layout” , International Journal of Modern Physics C vol. 18, no. 10 (2007), pp. 1537-1549, arXiv:0704.1748v5

Examples

>>> from numpy.random import seed, zipf
>>> seed(42)
>>> g = gt.random_graph(100, lambda: 3, directed=False)
>>> t = gt.min_spanning_tree(g)
>>> g.set_edge_filter(t)
>>> pos = gt.graph_draw(g, output=None) # initial configuration
>>> pos = gt.arf_layout(g, pos=pos, max_iter=0)
>>> gt.graph_draw(g, pos=pos, pin=True, output="graph-draw-arf.png")
<...>

ARF layout of a minimum spanning tree of a random graph.

graph_tool.draw.random_layout(g, shape=None, pos=None, dim=2)

Performs a random layout of the graph.

Parameters:

g : Graph

Graph to be used.

shape : tuple (optional, default: None)

Rectangular shape of the bounding area. If None, a square of linear size \sqrt{N} is used.

pos : PropertyMap (optional, default: None)

Vector vertex property maps where the coordinates should be stored.

dim : int (optional, default: 2)

Number of coordinates per vertex.

Returns:

pos : A vector vertex property map

Vertex property map with the coordinates of the vertices.

Notes

This algorithm has complexity O(V).