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A graph library

Project description


A simple graph library...
... A bit like networkx, just without the overhead...
... similar to graph-tool, without the Python 2.7 legacy...

import Graph
g = Graph()

That's it.

Available methods:

methods description
a in g assert if g contains node a
g.add_node(n, [obj]) adds a node (with a pointer to object obj if given)
g.node(node1) returns object attached to node 1.
g.del_node(node1) deletes node1 and all it's edges.
g.nodes() returns a list of nodes
len(g.nodes()) returns the number of nodes
g.nodes(from_node=1) returns nodes with edges from node 1
g.nodes(to_node=2) returns nodes with edges to node 2
g.nodes(in_degree=2) returns nodes with 2 incoming edges
g.nodes(out_degree=2) returns nodes with 2 outgoing edges
g.add_edge(1,2,3) adds edge to g for vector (1,2) with value 3
g.edge(1,2) returns value of edge between nodes 1 and 2
g.edge(1,2,default=3) returns default=3 if edge(1,2) doesn't exist.
similar to d.get(key, 3)
g.del_edge(1,2) removes edge between nodes 1 and 2
g.edges(path=[path]) returns a list of edges (along a path if given).
g.edges(from_node=1) returns edges outgoing from node 1
g.edges(to_node=2) returns edges incoming to node 2
len(g.edges()) returns the number of edges
g.from_dict(d) updates the graph from a dictionary
g.to_dict() dumps the graph as a dictionary
g.from_list(L) updates the graph from a list
g.to_list() dumps the graph as a list of edges
g.shortest_path(start,end) finds the path with smallest edge sum
g.breadth_first_search(start,end) finds the with least number of hops
g.depth_first_search(start,end) finds a path between 2 nodes (start, end) using DFS and backtracking.
g.distance_from_path(path) finds the distance following a given path.
g.maximum_flow(source,sink) finds the maximum flow between a source and a sink
g.solve_tsp() solves the traveling salesman problem for the graph
g.is_subgraph(g2) determines if graph g2 is a subgraph in g.
g.is_partite(n) determines if graph is n-partite
g.has_cycles() determines if there are cycles in the graph
g.same_path(p1,p2) compares two paths, returns True if they're the same.
g.adjacency_matrix() constructs the adjacency matrix for the graph.
g.all_pairs_shortest_paths() finds the shortest path between all nodes.
g.shortest_tree_all_pairs() finds the shortest tree for all pairs.
g.has_path(p) asserts whether a path p exists in g.
g.all_paths(start,end) finds all combinations of paths between 2 nodes.


want to doesn't work do instead but why?
have multiple edges between two nodes Graph(from_list=[(1,2,3), (1,2,4)] Add dummy nodes
[(1,a,3), (a,2,0),
Explicit is better than implicit.
multiple values on an edge g.add_edge(1,2,{'a':3, 'b':4}) Have two graphs
Most graph algorithms don't work with multiple values


Examples contains a number of tutorial/solutions to common operations research and computer science problems, which are made simple when treated as a graph.

module function description assignment_problem solves the assignment problem merkle_tree datablocks graph_hash computes the sha256 of a graphs nodes and edges flow_graph_hash computes the sha256 of a graph with multiple sources and sinks knapsack problem solves the knapsack problem weapons-target assignment problem solves the WTAP problem.

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