Logical Graph Builder that can be used for various problems that can be modeled as a graph data structure

coopgraph

Logical Graph Builder that can be used for various problems that can be modeled as a graph data structure

An Example:

from Graphs import Graph, Node
from dataStructs import Vector2

a = Node(name='A', pos=Vector2(0, 0))
b = Node(name='B', pos=Vector2(3, 3))
c = Node(name='C', pos=Vector2(2, 0))
d = Node(name='D', pos=Vector2(2, 1))
e = Node(name='E', pos=Vector2(3, 4))
f = Node(name='F', pos=Vector2(5, 5))

g = { a: [d],
b: [c],
c: [b, d, e],
d: [a, c],
e: [c, f],
f: []
}

graph = Graph(g)


The graph structure can then be used to perform various graph-related analysis:

Two find nodes that have no outbound connections

print(graph.find_isolated_vertices())


To find the shortest path between two nodes

print(graph.astar(a, e))


Note that for astar calculation, edges can be enabled or disabled against one or more disablers. This is useful for implementing temporary criteria in:

edges_to_disable = [value for key, value in graph.edges()][:3]

graph.disable_edges(edges_to_disable, "myDisabler")
path = graph.astar(a, e)
graph.disable_edges(edges_to_disable, "myDisabler")


you can also ignore disablers directly by passing a list of disabler names to the astar() method

edges_to_disable = [value for key, value in graph.edges()][:3]
graph.disable_edges(edges_to_disable, "myIngoredDisabler")

ignored = ["myIngoredDisabler"]
path = graph.astar(a, e, ignored_disablers=ignored)


An astar() call can also include custom g and h functions that allow for better control of the astar algorithm

def g(node1 Node, node2: Node) -> float:
if node1.pos - node2.pos > 10:
return 1
else
return .5

def h(node1 Node, node2: Node) -> float:
if node1.pos - node2.pos > 10:
return 100
else
return -100

path = graph.astar(a, e, g_func=g, h_func=h)


Project details

Uploaded source
Uploaded py3