digraphtools provides some tools for working with directed acyclic graphs, partial orders and topological sorting with Python.
digraphtools was written as a lightweight way of using DAGs and partial ordering to represent, sort and traverse dependency trees in a lightweight way.
The code is hosted on github at https://github.com/dbasden/python-digraphtools.
Graph Representation
Graphs
A graph is represented as a dict which maps a node to a list nodes connected via the outgoing edges of that node.
e.g.
graph = { 1: [2,3],
2: [3], 3: [] }
is a DAG represented by the edges (1,2) (1,3) (2,3) where the edge 2tuple is in the form of (from,to).
There are helper methods in deptools to generate graphs from a list of edges, and vice versa.
Binary relations
If a DAG represents dependencies, e.g. the edge (1,2) is taken to mean "1 depends on 2", this is backwards from a binary relation. (1,2) would be the relation 2P1.
Topological Sorting
There are two ways of generating linear extensions / topological sorts of dependencies (i.e. orders items must be processed in to satisfy dependency requirements):
deptools.dfs_topsort_traversal
deptools.dfs_topsort_traversal will take a graph and iterate over a single valid topologicaly sorted order
deptools.topsort.vr_topsort
deptools.topsort.vr_topsort will generate all valid linear extensions / topological orderings given an initial 'seed' linear extension (such as the one generated by deptools.dfs_topsort_traversal).
The method does not take the graph format as used by deptools as input, but it does have a helper method to generate it's input matrix from a partial order set (which can be generated from a graph using helpers in deptools).
See the examples in topsort.py and test/test_topsort.py for how to do this.
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Requirements:
· Python
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