## File topsort.py artifact 810c677434 part of check-in trunk

`# topsort - dependency (topological) sorting and cycle finding functions # Copyright (C) 2007 RADLogic # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; # version 2.1 of the License. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. # # See http://www.fsf.org/licensing/licenses/lgpl.txt for full license text. """Provide toplogical sorting (i.e. dependency sorting) functions. The topsort function is based on code posted on Usenet by Tim Peters. Modifications: - added doctests - changed some bits to use current Python idioms (listcomp instead of filter, +=/-=, inherit from Exception) - added a topsort_levels version that ports items in each dependency level into a sub-list - added find_cycles to aid in cycle debugging Run this module directly to run the doctests (unittests). Make sure they all pass before checking in any modifications. Requires Python >= 2.2 (For Python 2.2 also requires separate sets.py module) This requires the rad_util.py module. """ # Provide support for Python 2.2* from __future__ import generators __version__ = '$Revision: 0.9 $' __date__ = '$Date: 2007/03/27 04:15:26 $' __credits__ = '''Tim Peters -- original topsort code Tim Wegener -- doctesting, updating to current idioms, topsort_levels, find_cycles ''' # Make Python 2.3 sets look like Python 2.4 sets. try: set except NameError: from sets import Set as set from rad_util import is_rotated class CycleError(Exception): """Cycle Error""" pass def topsort(pairlist): """Topologically sort a list of (parent, child) pairs. Return a list of the elements in dependency order (parent to child order). >>> print topsort( [(1,2), (3,4), (5,6), (1,3), (1,5), (1,6), (2,5)] ) [1, 2, 3, 5, 4, 6] >>> print topsort( [(1,2), (1,3), (2,4), (3,4), (5,6), (4,5)] ) [1, 2, 3, 4, 5, 6] >>> print topsort( [(1,2), (2,3), (3,2)] ) Traceback (most recent call last): CycleError: ([1], {2: 1, 3: 1}, {2: [3], 3: [2]}) """ num_parents = {} # element -> # of predecessors children = {} # element -> list of successors for parent, child in pairlist: # Make sure every element is a key in num_parents. if not num_parents.has_key( parent ): num_parents[parent] = 0 if not num_parents.has_key( child ): num_parents[child] = 0 # Since child has a parent, increment child's num_parents count. num_parents[child] += 1 # ... and parent gains a child. children.setdefault(parent, []).append(child) # Suck up everything without a parent. answer = [x for x in num_parents.keys() if num_parents[x] == 0] # For everything in answer, knock down the parent count on its children. # Note that answer grows *in* the loop. for parent in answer: del num_parents[parent] if children.has_key( parent ): for child in children[parent]: num_parents[child] -= 1 if num_parents[child] == 0: answer.append( child ) # Following "del" isn't needed; just makes # CycleError details easier to grasp. del children[parent] if num_parents: # Everything in num_parents has at least one child -> # there's a cycle. raise CycleError(answer, num_parents, children) return answer def topsort_levels(pairlist): """Topologically sort a list of (parent, child) pairs into depth levels. This returns a generator. Turn this into a an iterator using the iter built-in function. (if you iterate over the iterator, each element gets generated when it is asked for, rather than generating the whole list up-front.) Each generated element is a list of items at that dependency level. >>> dependency_pairs = [(1,2), (3,4), (5,6), (1,3), (1,5), (1,6), (2,5)] >>> for level in iter(topsort_levels( dependency_pairs )): ... print level [1] [2, 3] [4, 5] [6] >>> dependency_pairs = [(1,2), (1,3), (2,4), (3,4), (5,6), (4,5)] >>> for level in iter(topsort_levels( dependency_pairs )): ... print level [1] [2, 3] [4] [5] [6] >>> dependency_pairs = [(1,2), (2,3), (3,4), (4, 3)] >>> try: ... for level in iter(topsort_levels( dependency_pairs )): ... print level ... except CycleError, exc: ... print 'CycleError:', exc [1] [2] CycleError: ({3: 1, 4: 1}, {3: [4], 4: [3]}) The cycle error should look like. CycleError: ({3: 1, 4: 1}, {3: [4], 4: [3]}) # todo: Make the doctest more robust (i.e. handle arbitrary dict order). """ num_parents = {} # element -> # of predecessors children = {} # element -> list of successors for parent, child in pairlist: # Make sure every element is a key in num_parents. if not num_parents.has_key( parent ): num_parents[parent] = 0 if not num_parents.has_key( child ): num_parents[child] = 0 # Since child has a parent, increment child's num_parents count. num_parents[child] += 1 # ... and parent gains a child. children.setdefault(parent, []).append(child) return topsort_levels_core(num_parents, children) def topsort_levels_core(num_parents, children): """Topologically sort a bunch of interdependent items based on dependency. This returns a generator. Turn this into a an iterator using the iter built-in function. (if you iterate over the iterator, each element gets generated when it is asked for, rather than generating the whole list up-front.) Each generated element is a list of items at that dependency level. >>> list(topsort_levels_core( ... {1: 0, 2: 1, 3: 1, 4: 1, 5: 2, 6: 2}, ... {1: [2, 3, 5, 6], 2: [5], 3: [4], 4: [], 5: [6]})) [[1], [2, 3], [4, 5], [6]] >>> list(topsort_levels_core( ... {1: 0, 2: 2, 3: 1}, ... {1: [2], 2: [3], 3: [2]})) Traceback (most recent call last): CycleError: ({2: 1, 3: 1}, {2: [3], 3: [2]}) This function has a more complicated interface than topsort_levels, but is useful if the data is easier to generate in this form. Arguments: num_parents -- key: item, value: number of parents (predecessors) children -- key: item, value: list of children (successors) """ while 1: # Suck up everything without a predecessor. level_parents = [x for x in num_parents.keys() if num_parents[x] == 0] if not level_parents: break # Offer the next generated item, # which is a list of the items at this dependency level. yield level_parents # For everything item in this level, # decrement the parent count, # since we have accounted for its parent. for level_parent in level_parents: del num_parents[level_parent] if children.has_key(level_parent): for level_parent_child in children[level_parent]: num_parents[level_parent_child] -= 1 del children[level_parent] if num_parents: # Everything in num_parents has at least one child -> # there's a cycle. raise CycleError(num_parents, children) else: # This is the end of the generator. raise StopIteration def find_cycles(parent_children): """Yield cycles. Each result is a list of items comprising a cycle. Use a 'stack' based approach to find all the cycles. This is a generator, so yields each cycle as it finds it. It is implicit that the last item in each cycle list is a parent of the first item (thereby forming a cycle). Arguments: parent_children -- parent -> collection of children Simplest cycle: >>> cycles = list(find_cycles({'A': ['B'], 'B': ['A']})) >>> len(cycles) 1 >>> cycle = cycles[0] >>> cycle.sort() >>> print cycle ['A', 'B'] Simplest cycle with extra baggage at the start and the end: >>> cycles = list(find_cycles(parent_children={'A': ['B'], ... 'B': ['C'], ... 'C': ['B', 'D'], ... 'D': [], ... })) >>> len(cycles) 1 >>> cycle = cycles[0] >>> cycle.sort() >>> print cycle ['B', 'C'] Double cycle: >>> cycles = list(find_cycles(parent_children={'A': ['B'], ... 'B': ['C1', 'C2'], ... 'C1': ['D1'], ... 'D1': ['E1'], ... 'E1': ['D1'], ... 'C2': ['D2'], ... 'D2': ['E2'], ... 'E2': ['D2'], ... })) >>> len(cycles) 2 >>> for cycle in cycles: ... cycle.sort() >>> cycles.sort() >>> cycle1 = cycles[0] >>> cycle1.sort() >>> print cycle1 ['D1', 'E1'] >>> cycle2 = cycles[1] >>> cycle2.sort() >>> print cycle2 ['D2', 'E2'] Simple cycle with children not specified for one item: # todo: Should this barf instead? >>> cycles = list(find_cycles(parent_children={'A': ['B'], ... 'B': ['A'], ... 'C': ['D']})) >>> len(cycles) 1 >>> cycle = cycles[0] >>> cycle.sort() >>> print cycle ['A', 'B'] Diamond cycle >>> cycles = list(find_cycles(parent_children={'A': ['B1', 'B2'], ... 'B1': ['C'], ... 'B2': ['C'], ... 'C': ['A', 'B1']})) >>> len(cycles) 3 >>> sorted_cycles = [] >>> for cycle in cycles: ... cycle = list(cycle) ... cycle.sort() ... sorted_cycles.append(cycle) >>> sorted_cycles.sort() >>> for cycle in sorted_cycles: ... print cycle ['A', 'B1', 'C'] ['A', 'B2', 'C'] ['B1', 'C'] Hairy case (order can matter if something is wrong): (Note order of B and C in the list.) >>> cycles = list(find_cycles(parent_children={ ... 'TD': ['DD'], ... 'TC': ['DC'], ... 'DC': ['DQ'], ... 'C': ['DQ'], ... 'DQ': ['IA', 'TO'], ... 'IA': ['A'], ... 'A': ['B', 'C'], ... })) >>> len(cycles) 1 >>> cycle = cycles[0] >>> cycle.sort() >>> print cycle ['A', 'C', 'DQ', 'IA'] """ cycles = [] visited_nodes = set() for parent in parent_children: if parent in visited_nodes: # This node is part of a path that has already been traversed. continue paths = [[parent]] while paths: path = paths.pop() parent = path[-1] try: children = parent_children[parent] except KeyError: continue for child in children: # Keeping a set of the path nodes, for O(1) lookups at the # expense of more memory and complexity, actually makes speed # worse. (Due to construction of sets.) # This is O(N). if child in path: # This is a cycle. cycle = path[path.index(child):] # Check that this is not a dup cycle. is_dup = False for other_cycle in cycles: if is_rotated(other_cycle, cycle): is_dup = True break if not is_dup: cycles.append(cycle) yield cycle else: # Push this new path onto the 'stack'. # This is probably the most expensive part of the algorithm # (a list copy). paths.append(path + [child]) # Mark the node as visited. visited_nodes.add(child) if __name__ == '__main__': # Run the doctest tests. import sys import doctest doctest.testmod(sys.modules['__main__'])`