We’ll use the graphviz library to generate DOT-formatted data, and the dot command to generate an image:
import subprocess import graphviz _RENDER_CMD = ['dot'] _FORMAT = 'png' def build(): comment = "Test comment" dot = graphviz.Digraph(comment=comment) dot.node('P', label='Parent') dot.node('G1C1', label='Gen 1 Child 1') dot.node('G1C2', label='Gen 1 Child 2') dot.node('G2C1', label='Gen 2 Child 1') dot.node('G2C2', label='Gen 2 Child 2') dot.edge('P', 'G1C1') dot.edge('P', 'G1C2') dot.edge('G1C2', 'G2C1') dot.edge('G1C2', 'G2C2') return dot def get_image_data(dot): cmd = _RENDER_CMD + ['-T' + _FORMAT] p = subprocess.Popen( cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) (stdout, stderr) = p.communicate(input=dot) r = p.wait() if r != 0: raise ValueError("Command failed (%d):n" "Standard output:n%sn" "Standard error:n%s" % (r, stdout, stderr)) return stdout dot = build() dot_data = get_image_data(dot.source) with open('output.png', 'wb') as f: f.write(dot_data)
Note that we can provide labels for the edges, too. However, they tend to crowd the actual edges and it has turned out to be non-trivial to add margins to them:
Note that there are other render commands available for different requirements. This list is from the homepage:
- dot – “hierarchical” or layered drawings of directed graphs. This is the default tool to use if edges have directionality.
- neato – “spring model” layouts. This is the default tool to use if the graph is not too large (about 100 nodes) and you don’t know anything else about it. Neato attempts to minimize a global energy function, which is equivalent to statistical multi-dimensional scaling.
- fdp – “spring model” layouts similar to those of neato, but does this by reducing forces rather than working with energy.
- sfdp – multiscale version of fdp for the layout of large graphs.
- twopi – radial layouts, after Graham Wills 97. Nodes are placed on concentric circles depending their distance from a given root node.
- circo – circular layout, after Six and Tollis 99, Kauffman and Wiese 02. This is suitable for certain diagrams of multiple cyclic structures, such as certain telecommunications networks.
This is an example of sfdp with the “-Goverlap=scale” argument with a very large graph (zoomed out).
If you’re running OS X, I had to uninstall graphviz, install the gts library, and then reinstall graphviz with an extra option to bind it:
$ brew uninstall graphviz $ brew install gts $ brew install --with-gts graphviz
If graphviz hasn’t been built with gts, you will get the following error:
Standard error: Error: remove_overlap: Graphviz not built with triangulation library
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