<NME> tarjan_sort.py
<BEF> 
# Downloaded from http://www.logarithmic.net/pfh-files/blog/01208083168/sort.py

"""
   
   Tarjan's algorithm and topological sorting implementation in Python
   
   by Paul Harrison
   
   Public domain, do with it as you will

"""

def strongly_connected_components(graph):
    """ Find the strongly connected components in a graph using
        Tarjan's algorithm.
        
        graph should be a dictionary mapping node names to
        lists of successor nodes.
        """
    
    result = [ ]
    stack = [ ]
    low = { }
        
    def visit(node):
        if node in low: return
        if node not in graph: graph[node] = []

        num = len(low)
        low[node] = num
        stack_pos = len(stack)
        stack.append(node)
        
        for successor in graph[node]:
            visit(successor)
            low[node] = min(low[node], low[successor])
        
        if num == low[node]:
            component = tuple(stack[stack_pos:])
            del stack[stack_pos:]
            result.append(component)
            for item in component:
                low[item] = len(graph)
    
    for node in dict(graph):
        visit(node)
    
    return result


def topological_sort(graph):
    count = { }
    for node in graph:
        count[node] = 0
    for node in graph:
        for successor in graph[node]:
            count[successor] += 1

    ready = [ node for node in graph if count[node] == 0 ]
    
    result = [ ]
    while ready:
        node = ready.pop(-1)
        result.append(node)
        
        for successor in graph[node]:
            count[successor] -= 1
            if count[successor] == 0:
                ready.append(successor)
    
    return result


def robust_topological_sort(graph):
    """ First identify strongly connected components,
        then perform a topological sort on these components. """

    components = strongly_connected_components(graph)

    node_component = { }
    for component in components:
        for node in component:
            node_component[node] = component

    component_graph = { }
    for component in components:
        component_graph[component] = [ ]
    
    for node in graph:
        node_c = node_component[node]
        for successor in graph[node]:
            successor_c = node_component[successor]
            if node_c != successor_c:
                component_graph[node_c].append(successor_c) 

    return topological_sort(component_graph)


if __name__ == '__main__':
    d = {
        0 : [1],
        1 : [2],
        2 : [1,3],
        3 : [3],
    }
    #print robust_topological_sort(d)

    d = {0 : [1, 2, 4], 1 : [3, 4], 2 : [0, 3], 3 : [], 4: [1]}
    print d
    print "scc", strongly_connected_components(d)
    print "rts", robust_topological_sort(d)



<MSG> Ported Diaphora to IDA 7.4 + Python 3

CORE: Ported Diaphora to Python 3.X and IDA 7.4
LICENSE: Changed license from GPL v3 to AGPL v3.

<DFF> @@ -107,7 +107,7 @@ if __name__ == '__main__':
     #print robust_topological_sort(d)
 
     d = {0 : [1, 2, 4], 1 : [3, 4], 2 : [0, 3], 3 : [], 4: [1]}
-    print d
-    print "scc", strongly_connected_components(d)
-    print "rts", robust_topological_sort(d)
+    print(d)
+    print("scc", strongly_connected_components(d))
+    print("rts", robust_topological_sort(d))
 

Ported Diaphora to IDA 7.4 + Python 3