-- (c) The University of Glasgow 2006 {-# LANGUAGE CPP, ScopedTypeVariables #-} module Digraph( Graph, graphFromEdgedVertices, SCC(..), Node, flattenSCC, flattenSCCs, stronglyConnCompG, topologicalSortG, dfsTopSortG, verticesG, edgesG, hasVertexG, reachableG, reachablesG, transposeG, outdegreeG, indegreeG, vertexGroupsG, emptyG, componentsG, findCycle, -- For backwards compatability with the simpler version of Digraph stronglyConnCompFromEdgedVertices, stronglyConnCompFromEdgedVerticesR, ) where #include "HsVersions.h" ------------------------------------------------------------------------------ -- A version of the graph algorithms described in: -- -- ``Lazy Depth-First Search and Linear IntGraph Algorithms in Haskell'' -- by David King and John Launchbury -- -- Also included is some additional code for printing tree structures ... -- -- If you ever find yourself in need of algorithms for classifying edges, -- or finding connected/biconnected components, consult the history; Sigbjorn -- Finne contributed some implementations in 1997, although we've since -- removed them since they were not used anywhere in GHC. ------------------------------------------------------------------------------ import Util ( minWith, count ) import Outputable import Maybes ( expectJust ) import MonadUtils ( allM ) -- Extensions import Control.Monad ( filterM, liftM, liftM2 ) import Control.Monad.ST -- std interfaces import Data.Maybe import Data.Array import Data.List hiding (transpose) import Data.Array.ST import qualified Data.Map as Map import qualified Data.Set as Set import qualified Data.Graph as G import Data.Graph hiding (Graph, Edge, transposeG, reachable) import Data.Tree {- ************************************************************************ * * * Graphs and Graph Construction * * ************************************************************************ Note [Nodes, keys, vertices] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ * A 'node' is a big blob of client-stuff * Each 'node' has a unique (client) 'key', but the latter is in Ord and has fast comparison * Digraph then maps each 'key' to a Vertex (Int) which is arranged densely in 0.n -} data Graph node = Graph { gr_int_graph :: IntGraph, gr_vertex_to_node :: Vertex -> node, gr_node_to_vertex :: node -> Maybe Vertex } data Edge node = Edge node node type Node key payload = (payload, key, [key]) -- The payload is user data, just carried around in this module -- The keys are ordered -- The [key] are the dependencies of the node; -- it's ok to have extra keys in the dependencies that -- are not the key of any Node in the graph emptyGraph :: Graph a emptyGraph = Graph (array (1, 0) []) (error "emptyGraph") (const Nothing) graphFromEdgedVertices :: Ord key -- We only use Ord for efficiency, -- it doesn't effect the result, so -- it can be safely used with Unique's. => [Node key payload] -- The graph; its ok for the -- out-list to contain keys which arent -- a vertex key, they are ignored -> Graph (Node key payload) graphFromEdgedVertices [] = emptyGraph graphFromEdgedVertices edged_vertices = Graph graph vertex_fn (key_vertex . key_extractor) where key_extractor (_, k, _) = k (bounds, vertex_fn, key_vertex, numbered_nodes) = reduceNodesIntoVertices edged_vertices key_extractor graph = array bounds [ (v, sort $ mapMaybe key_vertex ks) | (v, (_, _, ks)) <- numbered_nodes] -- We normalize outgoing edges by sorting on node order, so -- that the result doesn't depend on the order of the edges reduceNodesIntoVertices :: Ord key => [node] -> (node -> key) -> (Bounds, Vertex -> node, key -> Maybe Vertex, [(Vertex, node)]) reduceNodesIntoVertices nodes key_extractor = (bounds, (!) vertex_map, key_vertex, numbered_nodes) where max_v = length nodes - 1 bounds = (0, max_v) :: (Vertex, Vertex) -- Keep the order intact to make the result depend on input order -- instead of key order numbered_nodes = zip [0..] nodes vertex_map = array bounds numbered_nodes key_map = Map.fromList [ (key_extractor node, v) | (v, node) <- numbered_nodes ] key_vertex k = Map.lookup k key_map {- ************************************************************************ * * * SCC * * ************************************************************************ -} type WorkItem key payload = (Node key payload, -- Tip of the path [payload]) -- Rest of the path; -- [a,b,c] means c depends on b, b depends on a -- | Find a reasonably short cycle a->b->c->a, in a strongly -- connected component. The input nodes are presumed to be -- a SCC, so you can start anywhere. findCycle :: forall payload key. Ord key => [Node key payload] -- The nodes. The dependencies can -- contain extra keys, which are ignored -> Maybe [payload] -- A cycle, starting with node -- so each depends on the next findCycle graph = go Set.empty (new_work root_deps []) [] where env :: Map.Map key (Node key payload) env = Map.fromList [ (key, node) | node@(_, key, _) <- graph ] -- Find the node with fewest dependencies among the SCC modules -- This is just a heuristic to find some plausible root module root :: Node key payload root = fst (minWith snd [ (node, count (`Map.member` env) deps) | node@(_,_,deps) <- graph ]) (root_payload,root_key,root_deps) = root -- 'go' implements Dijkstra's algorithm, more or less go :: Set.Set key -- Visited -> [WorkItem key payload] -- Work list, items length n -> [WorkItem key payload] -- Work list, items length n+1 -> Maybe [payload] -- Returned cycle -- Invariant: in a call (go visited ps qs), -- visited = union (map tail (ps ++ qs)) go _ [] [] = Nothing -- No cycles go visited [] qs = go visited qs [] go visited (((payload,key,deps), path) : ps) qs | key == root_key = Just (root_payload : reverse path) | key `Set.member` visited = go visited ps qs | key `Map.notMember` env = go visited ps qs | otherwise = go (Set.insert key visited) ps (new_qs ++ qs) where new_qs = new_work deps (payload : path) new_work :: [key] -> [payload] -> [WorkItem key payload] new_work deps path = [ (n, path) | Just n <- map (`Map.lookup` env) deps ] {- ************************************************************************ * * * Strongly Connected Component wrappers for Graph * * ************************************************************************ Note: the components are returned topologically sorted: later components depend on earlier ones, but not vice versa i.e. later components only have edges going from them to earlier ones. -} stronglyConnCompG :: Graph node -> [SCC node] stronglyConnCompG graph = decodeSccs graph forest where forest = {-# SCC "Digraph.scc" #-} scc (gr_int_graph graph) decodeSccs :: Graph node -> Forest Vertex -> [SCC node] decodeSccs Graph { gr_int_graph = graph, gr_vertex_to_node = vertex_fn } forest = map decode forest where decode (Node v []) | mentions_itself v = CyclicSCC [vertex_fn v] | otherwise = AcyclicSCC (vertex_fn v) decode other = CyclicSCC (dec other []) where dec (Node v ts) vs = vertex_fn v : foldr dec vs ts mentions_itself v = v `elem` (graph ! v) -- The following two versions are provided for backwards compatability: stronglyConnCompFromEdgedVertices :: Ord key => [Node key payload] -> [SCC payload] stronglyConnCompFromEdgedVertices = map (fmap get_node) . stronglyConnCompFromEdgedVerticesR where get_node (n, _, _) = n -- The "R" interface is used when you expect to apply SCC to -- (some of) the result of SCC, so you dont want to lose the dependency info stronglyConnCompFromEdgedVerticesR :: Ord key => [Node key payload] -> [SCC (Node key payload)] stronglyConnCompFromEdgedVerticesR = stronglyConnCompG . graphFromEdgedVertices {- ************************************************************************ * * * Misc wrappers for Graph * * ************************************************************************ -} topologicalSortG :: Graph node -> [node] topologicalSortG graph = map (gr_vertex_to_node graph) result where result = {-# SCC "Digraph.topSort" #-} topSort (gr_int_graph graph) dfsTopSortG :: Graph node -> [[node]] dfsTopSortG graph = map (map (gr_vertex_to_node graph) . flatten) $ dfs g (topSort g) where g = gr_int_graph graph reachableG :: Graph node -> node -> [node] reachableG graph from = map (gr_vertex_to_node graph) result where from_vertex = expectJust "reachableG" (gr_node_to_vertex graph from) result = {-# SCC "Digraph.reachable" #-} reachable (gr_int_graph graph) [from_vertex] reachablesG :: Graph node -> [node] -> [node] reachablesG graph froms = map (gr_vertex_to_node graph) result where result = {-# SCC "Digraph.reachable" #-} reachable (gr_int_graph graph) vs vs = [ v | Just v <- map (gr_node_to_vertex graph) froms ] hasVertexG :: Graph node -> node -> Bool hasVertexG graph node = isJust $ gr_node_to_vertex graph node verticesG :: Graph node -> [node] verticesG graph = map (gr_vertex_to_node graph) $ vertices (gr_int_graph graph) edgesG :: Graph node -> [Edge node] edgesG graph = map (\(v1, v2) -> Edge (v2n v1) (v2n v2)) $ edges (gr_int_graph graph) where v2n = gr_vertex_to_node graph transposeG :: Graph node -> Graph node transposeG graph = Graph (G.transposeG (gr_int_graph graph)) (gr_vertex_to_node graph) (gr_node_to_vertex graph) outdegreeG :: Graph node -> node -> Maybe Int outdegreeG = degreeG outdegree indegreeG :: Graph node -> node -> Maybe Int indegreeG = degreeG indegree degreeG :: (G.Graph -> Table Int) -> Graph node -> node -> Maybe Int degreeG degree graph node = let table = degree (gr_int_graph graph) in fmap ((!) table) $ gr_node_to_vertex graph node vertexGroupsG :: Graph node -> [[node]] vertexGroupsG graph = map (map (gr_vertex_to_node graph)) result where result = vertexGroups (gr_int_graph graph) emptyG :: Graph node -> Bool emptyG g = graphEmpty (gr_int_graph g) componentsG :: Graph node -> [[node]] componentsG graph = map (map (gr_vertex_to_node graph) . flatten) $ components (gr_int_graph graph) {- ************************************************************************ * * * Showing Graphs * * ************************************************************************ -} instance Outputable node => Outputable (Graph node) where ppr graph = vcat [ hang (text "Vertices:") 2 (vcat (map ppr $ verticesG graph)), hang (text "Edges:") 2 (vcat (map ppr $ edgesG graph)) ] instance Outputable node => Outputable (Edge node) where ppr (Edge from to) = ppr from <+> text "->" <+> ppr to graphEmpty :: G.Graph -> Bool graphEmpty g = lo > hi where (lo, hi) = bounds g {- ************************************************************************ * * * IntGraphs * * ************************************************************************ -} type IntGraph = G.Graph {- ------------------------------------------------------------ -- Depth first search numbering ------------------------------------------------------------ -} -- Data.Tree has flatten for Tree, but nothing for Forest preorderF :: Forest a -> [a] preorderF ts = concat (map flatten ts) {- ------------------------------------------------------------ -- Finding reachable vertices ------------------------------------------------------------ -} -- This generalizes reachable which was found in Data.Graph reachable :: IntGraph -> [Vertex] -> [Vertex] reachable g vs = preorderF (dfs g vs) {- ------------------------------------------------------------ -- Total ordering on groups of vertices ------------------------------------------------------------ The plan here is to extract a list of groups of elements of the graph such that each group has no dependence except on nodes in previous groups (i.e. in particular they may not depend on nodes in their own group) and is maximal such group. Clearly we cannot provide a solution for cyclic graphs. We proceed by iteratively removing elements with no outgoing edges and their associated edges from the graph. This probably isn't very efficient and certainly isn't very clever. -} type Set s = STArray s Vertex Bool mkEmpty :: Bounds -> ST s (Set s) mkEmpty bnds = newArray bnds False contains :: Set s -> Vertex -> ST s Bool contains m v = readArray m v include :: Set s -> Vertex -> ST s () include m v = writeArray m v True vertexGroups :: IntGraph -> [[Vertex]] vertexGroups g = runST (mkEmpty (bounds g) >>= \provided -> vertexGroupsS provided g next_vertices) where next_vertices = noOutEdges g noOutEdges :: IntGraph -> [Vertex] noOutEdges g = [ v | v <- vertices g, null (g!v)] vertexGroupsS :: Set s -> IntGraph -> [Vertex] -> ST s [[Vertex]] vertexGroupsS provided g to_provide = if null to_provide then do { all_provided <- allM (provided `contains`) (vertices g) ; if all_provided then return [] else error "vertexGroup: cyclic graph" } else do { mapM_ (include provided) to_provide ; to_provide' <- filterM (vertexReady provided g) (vertices g) ; rest <- vertexGroupsS provided g to_provide' ; return $ to_provide : rest } vertexReady :: Set s -> IntGraph -> Vertex -> ST s Bool vertexReady provided g v = liftM2 (&&) (liftM not $ provided `contains` v) (allM (provided `contains`) (g!v))