7 lines
137 KiB
Text
Executable file
7 lines
137 KiB
Text
Executable file
{
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"version": 3,
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"sources": ["../../node_modules/dagre-d3-es/src/dagre/util.js", "../../node_modules/dagre-d3-es/src/dagre/add-border-segments.js", "../../node_modules/dagre-d3-es/src/dagre/coordinate-system.js", "../../node_modules/dagre-d3-es/src/dagre/data/list.js", "../../node_modules/dagre-d3-es/src/dagre/greedy-fas.js", "../../node_modules/dagre-d3-es/src/dagre/acyclic.js", "../../node_modules/dagre-d3-es/src/dagre/normalize.js", "../../node_modules/dagre-d3-es/src/dagre/rank/util.js", "../../node_modules/dagre-d3-es/src/dagre/rank/feasible-tree.js", "../../node_modules/dagre-d3-es/src/graphlib/alg/dijkstra.js", "../../node_modules/dagre-d3-es/src/graphlib/alg/floyd-warshall.js", "../../node_modules/dagre-d3-es/src/graphlib/alg/topsort.js", "../../node_modules/dagre-d3-es/src/graphlib/alg/dfs.js", "../../node_modules/dagre-d3-es/src/graphlib/alg/postorder.js", "../../node_modules/dagre-d3-es/src/graphlib/alg/preorder.js", "../../node_modules/dagre-d3-es/src/dagre/rank/network-simplex.js", "../../node_modules/dagre-d3-es/src/dagre/rank/index.js", "../../node_modules/dagre-d3-es/src/dagre/nesting-graph.js", "../../node_modules/dagre-d3-es/src/dagre/order/add-subgraph-constraints.js", "../../node_modules/dagre-d3-es/src/dagre/order/build-layer-graph.js", "../../node_modules/dagre-d3-es/src/dagre/order/cross-count.js", "../../node_modules/dagre-d3-es/src/dagre/order/init-order.js", "../../node_modules/dagre-d3-es/src/dagre/order/barycenter.js", "../../node_modules/dagre-d3-es/src/dagre/order/resolve-conflicts.js", "../../node_modules/dagre-d3-es/src/dagre/order/sort.js", "../../node_modules/dagre-d3-es/src/dagre/order/sort-subgraph.js", "../../node_modules/dagre-d3-es/src/dagre/order/index.js", "../../node_modules/dagre-d3-es/src/dagre/parent-dummy-chains.js", "../../node_modules/dagre-d3-es/src/dagre/position/bk.js", "../../node_modules/dagre-d3-es/src/dagre/position/index.js", "../../node_modules/dagre-d3-es/src/dagre/layout.js"],
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"sourcesContent": ["import * as _ from 'lodash-es';\nimport { Graph } from '../graphlib/index.js';\n\nexport {\n addDummyNode,\n simplify,\n asNonCompoundGraph,\n successorWeights,\n predecessorWeights,\n intersectRect,\n buildLayerMatrix,\n normalizeRanks,\n removeEmptyRanks,\n addBorderNode,\n maxRank,\n partition,\n time,\n notime,\n};\n\n/*\n * Adds a dummy node to the graph and return v.\n */\nfunction addDummyNode(g, type, attrs, name) {\n var v;\n do {\n v = _.uniqueId(name);\n } while (g.hasNode(v));\n\n attrs.dummy = type;\n g.setNode(v, attrs);\n return v;\n}\n\n/*\n * Returns a new graph with only simple edges. Handles aggregation of data\n * associated with multi-edges.\n */\nfunction simplify(g) {\n var simplified = new Graph().setGraph(g.graph());\n _.forEach(g.nodes(), function (v) {\n simplified.setNode(v, g.node(v));\n });\n _.forEach(g.edges(), function (e) {\n var simpleLabel = simplified.edge(e.v, e.w) || { weight: 0, minlen: 1 };\n var label = g.edge(e);\n simplified.setEdge(e.v, e.w, {\n weight: simpleLabel.weight + label.weight,\n minlen: Math.max(simpleLabel.minlen, label.minlen),\n });\n });\n return simplified;\n}\n\nfunction asNonCompoundGraph(g) {\n var simplified = new Graph({ multigraph: g.isMultigraph() }).setGraph(g.graph());\n _.forEach(g.nodes(), function (v) {\n if (!g.children(v).length) {\n simplified.setNode(v, g.node(v));\n }\n });\n _.forEach(g.edges(), function (e) {\n simplified.setEdge(e, g.edge(e));\n });\n return simplified;\n}\n\nfunction successorWeights(g) {\n var weightMap = _.map(g.nodes(), function (v) {\n var sucs = {};\n _.forEach(g.outEdges(v), function (e) {\n sucs[e.w] = (sucs[e.w] || 0) + g.edge(e).weight;\n });\n return sucs;\n });\n return _.zipObject(g.nodes(), weightMap);\n}\n\nfunction predecessorWeights(g) {\n var weightMap = _.map(g.nodes(), function (v) {\n var preds = {};\n _.forEach(g.inEdges(v), function (e) {\n preds[e.v] = (preds[e.v] || 0) + g.edge(e).weight;\n });\n return preds;\n });\n return _.zipObject(g.nodes(), weightMap);\n}\n\n/*\n * Finds where a line starting at point ({x, y}) would intersect a rectangle\n * ({x, y, width, height}) if it were pointing at the rectangle's center.\n */\nfunction intersectRect(rect, point) {\n var x = rect.x;\n var y = rect.y;\n\n // Rectangle intersection algorithm from:\n // http://math.stackexchange.com/questions/108113/find-edge-between-two-boxes\n var dx = point.x - x;\n var dy = point.y - y;\n var w = rect.width / 2;\n var h = rect.height / 2;\n\n if (!dx && !dy) {\n throw new Error('Not possible to find intersection inside of the rectangle');\n }\n\n var sx, sy;\n if (Math.abs(dy) * w > Math.abs(dx) * h) {\n // Intersection is top or bottom of rect.\n if (dy < 0) {\n h = -h;\n }\n sx = (h * dx) / dy;\n sy = h;\n } else {\n // Intersection is left or right of rect.\n if (dx < 0) {\n w = -w;\n }\n sx = w;\n sy = (w * dy) / dx;\n }\n\n return { x: x + sx, y: y + sy };\n}\n\n/*\n * Given a DAG with each node assigned \"rank\" and \"order\" properties, this\n * function will produce a matrix with the ids of each node.\n */\nfunction buildLayerMatrix(g) {\n var layering = _.map(_.range(maxRank(g) + 1), function () {\n return [];\n });\n _.forEach(g.nodes(), function (v) {\n var node = g.node(v);\n var rank = node.rank;\n if (!_.isUndefined(rank)) {\n layering[rank][node.order] = v;\n }\n });\n return layering;\n}\n\n/*\n * Adjusts the ranks for all nodes in the graph such that all nodes v have\n * rank(v) >= 0 and at least one node w has rank(w) = 0.\n */\nfunction normalizeRanks(g) {\n var min = _.min(\n _.map(g.nodes(), function (v) {\n return g.node(v).rank;\n })\n );\n _.forEach(g.nodes(), function (v) {\n var node = g.node(v);\n if (_.has(node, 'rank')) {\n node.rank -= min;\n }\n });\n}\n\nfunction removeEmptyRanks(g) {\n // Ranks may not start at 0, so we need to offset them\n var offset = _.min(\n _.map(g.nodes(), function (v) {\n return g.node(v).rank;\n })\n );\n\n var layers = [];\n _.forEach(g.nodes(), function (v) {\n var rank = g.node(v).rank - offset;\n if (!layers[rank]) {\n layers[rank] = [];\n }\n layers[rank].push(v);\n });\n\n var delta = 0;\n var nodeRankFactor = g.graph().nodeRankFactor;\n _.forEach(layers, function (vs, i) {\n if (_.isUndefined(vs) && i % nodeRankFactor !== 0) {\n --delta;\n } else if (delta) {\n _.forEach(vs, function (v) {\n g.node(v).rank += delta;\n });\n }\n });\n}\n\nfunction addBorderNode(g, prefix, rank, order) {\n var node = {\n width: 0,\n height: 0,\n };\n if (arguments.length >= 4) {\n node.rank = rank;\n node.order = order;\n }\n return addDummyNode(g, 'border', node, prefix);\n}\n\nfunction maxRank(g) {\n return _.max(\n _.map(g.nodes(), function (v) {\n var rank = g.node(v).rank;\n if (!_.isUndefined(rank)) {\n return rank;\n }\n })\n );\n}\n\n/*\n * Partition a collection into two groups: `lhs` and `rhs`. If the supplied\n * function returns true for an entry it goes into `lhs`. Otherwise it goes\n * into `rhs.\n */\nfunction partition(collection, fn) {\n var result = { lhs: [], rhs: [] };\n _.forEach(collection, function (value) {\n if (fn(value)) {\n result.lhs.push(value);\n } else {\n result.rhs.push(value);\n }\n });\n return result;\n}\n\n/*\n * Returns a new function that wraps `fn` with a timer. The wrapper logs the\n * time it takes to execute the function.\n */\nfunction time(name, fn) {\n var start = _.now();\n try {\n return fn();\n } finally {\n console.log(name + ' time: ' + (_.now() - start) + 'ms');\n }\n}\n\nfunction notime(name, fn) {\n return fn();\n}\n", "import * as _ from 'lodash-es';\nimport * as util from './util.js';\n\nexport { addBorderSegments };\n\nfunction addBorderSegments(g) {\n function dfs(v) {\n var children = g.children(v);\n var node = g.node(v);\n if (children.length) {\n _.forEach(children, dfs);\n }\n\n if (_.has(node, 'minRank')) {\n node.borderLeft = [];\n node.borderRight = [];\n for (var rank = node.minRank, maxRank = node.maxRank + 1; rank < maxRank; ++rank) {\n addBorderNode(g, 'borderLeft', '_bl', v, node, rank);\n addBorderNode(g, 'borderRight', '_br', v, node, rank);\n }\n }\n }\n\n _.forEach(g.children(), dfs);\n}\n\nfunction addBorderNode(g, prop, prefix, sg, sgNode, rank) {\n var label = { width: 0, height: 0, rank: rank, borderType: prop };\n var prev = sgNode[prop][rank - 1];\n var curr = util.addDummyNode(g, 'border', label, prefix);\n sgNode[prop][rank] = curr;\n g.setParent(curr, sg);\n if (prev) {\n g.setEdge(prev, curr, { weight: 1 });\n }\n}\n", "import * as _ from 'lodash-es';\n\nexport { adjust, undo };\n\nfunction adjust(g) {\n var rankDir = g.graph().rankdir.toLowerCase();\n if (rankDir === 'lr' || rankDir === 'rl') {\n swapWidthHeight(g);\n }\n}\n\nfunction undo(g) {\n var rankDir = g.graph().rankdir.toLowerCase();\n if (rankDir === 'bt' || rankDir === 'rl') {\n reverseY(g);\n }\n\n if (rankDir === 'lr' || rankDir === 'rl') {\n swapXY(g);\n swapWidthHeight(g);\n }\n}\n\nfunction swapWidthHeight(g) {\n _.forEach(g.nodes(), function (v) {\n swapWidthHeightOne(g.node(v));\n });\n _.forEach(g.edges(), function (e) {\n swapWidthHeightOne(g.edge(e));\n });\n}\n\nfunction swapWidthHeightOne(attrs) {\n var w = attrs.width;\n attrs.width = attrs.height;\n attrs.height = w;\n}\n\nfunction reverseY(g) {\n _.forEach(g.nodes(), function (v) {\n reverseYOne(g.node(v));\n });\n\n _.forEach(g.edges(), function (e) {\n var edge = g.edge(e);\n _.forEach(edge.points, reverseYOne);\n if (_.has(edge, 'y')) {\n reverseYOne(edge);\n }\n });\n}\n\nfunction reverseYOne(attrs) {\n attrs.y = -attrs.y;\n}\n\nfunction swapXY(g) {\n _.forEach(g.nodes(), function (v) {\n swapXYOne(g.node(v));\n });\n\n _.forEach(g.edges(), function (e) {\n var edge = g.edge(e);\n _.forEach(edge.points, swapXYOne);\n if (_.has(edge, 'x')) {\n swapXYOne(edge);\n }\n });\n}\n\nfunction swapXYOne(attrs) {\n var x = attrs.x;\n attrs.x = attrs.y;\n attrs.y = x;\n}\n", "/*\n * Simple doubly linked list implementation derived from Cormen, et al.,\n * \"Introduction to Algorithms\".\n */\n\nexport { List };\n\nclass List {\n constructor() {\n var sentinel = {};\n sentinel._next = sentinel._prev = sentinel;\n this._sentinel = sentinel;\n }\n dequeue() {\n var sentinel = this._sentinel;\n var entry = sentinel._prev;\n if (entry !== sentinel) {\n unlink(entry);\n return entry;\n }\n }\n enqueue(entry) {\n var sentinel = this._sentinel;\n if (entry._prev && entry._next) {\n unlink(entry);\n }\n entry._next = sentinel._next;\n sentinel._next._prev = entry;\n sentinel._next = entry;\n entry._prev = sentinel;\n }\n toString() {\n var strs = [];\n var sentinel = this._sentinel;\n var curr = sentinel._prev;\n while (curr !== sentinel) {\n strs.push(JSON.stringify(curr, filterOutLinks));\n curr = curr._prev;\n }\n return '[' + strs.join(', ') + ']';\n }\n}\n\nfunction unlink(entry) {\n entry._prev._next = entry._next;\n entry._next._prev = entry._prev;\n delete entry._next;\n delete entry._prev;\n}\n\nfunction filterOutLinks(k, v) {\n if (k !== '_next' && k !== '_prev') {\n return v;\n }\n}\n", "import * as _ from 'lodash-es';\nimport { Graph } from '../graphlib/index.js';\nimport { List } from './data/list.js';\n\n/*\n * A greedy heuristic for finding a feedback arc set for a graph. A feedback\n * arc set is a set of edges that can be removed to make a graph acyclic.\n * The algorithm comes from: P. Eades, X. Lin, and W. F. Smyth, \"A fast and\n * effective heuristic for the feedback arc set problem.\" This implementation\n * adjusts that from the paper to allow for weighted edges.\n */\nexport { greedyFAS };\n\nvar DEFAULT_WEIGHT_FN = _.constant(1);\n\nfunction greedyFAS(g, weightFn) {\n if (g.nodeCount() <= 1) {\n return [];\n }\n var state = buildState(g, weightFn || DEFAULT_WEIGHT_FN);\n var results = doGreedyFAS(state.graph, state.buckets, state.zeroIdx);\n\n // Expand multi-edges\n return _.flatten(\n _.map(results, function (e) {\n return g.outEdges(e.v, e.w);\n })\n );\n}\n\nfunction doGreedyFAS(g, buckets, zeroIdx) {\n var results = [];\n var sources = buckets[buckets.length - 1];\n var sinks = buckets[0];\n\n var entry;\n while (g.nodeCount()) {\n while ((entry = sinks.dequeue())) {\n removeNode(g, buckets, zeroIdx, entry);\n }\n while ((entry = sources.dequeue())) {\n removeNode(g, buckets, zeroIdx, entry);\n }\n if (g.nodeCount()) {\n for (var i = buckets.length - 2; i > 0; --i) {\n entry = buckets[i].dequeue();\n if (entry) {\n results = results.concat(removeNode(g, buckets, zeroIdx, entry, true));\n break;\n }\n }\n }\n }\n\n return results;\n}\n\nfunction removeNode(g, buckets, zeroIdx, entry, collectPredecessors) {\n var results = collectPredecessors ? [] : undefined;\n\n _.forEach(g.inEdges(entry.v), function (edge) {\n var weight = g.edge(edge);\n var uEntry = g.node(edge.v);\n\n if (collectPredecessors) {\n results.push({ v: edge.v, w: edge.w });\n }\n\n uEntry.out -= weight;\n assignBucket(buckets, zeroIdx, uEntry);\n });\n\n _.forEach(g.outEdges(entry.v), function (edge) {\n var weight = g.edge(edge);\n var w = edge.w;\n var wEntry = g.node(w);\n wEntry['in'] -= weight;\n assignBucket(buckets, zeroIdx, wEntry);\n });\n\n g.removeNode(entry.v);\n\n return results;\n}\n\nfunction buildState(g, weightFn) {\n var fasGraph = new Graph();\n var maxIn = 0;\n var maxOut = 0;\n\n _.forEach(g.nodes(), function (v) {\n fasGraph.setNode(v, { v: v, in: 0, out: 0 });\n });\n\n // Aggregate weights on nodes, but also sum the weights across multi-edges\n // into a single edge for the fasGraph.\n _.forEach(g.edges(), function (e) {\n var prevWeight = fasGraph.edge(e.v, e.w) || 0;\n var weight = weightFn(e);\n var edgeWeight = prevWeight + weight;\n fasGraph.setEdge(e.v, e.w, edgeWeight);\n maxOut = Math.max(maxOut, (fasGraph.node(e.v).out += weight));\n maxIn = Math.max(maxIn, (fasGraph.node(e.w)['in'] += weight));\n });\n\n var buckets = _.range(maxOut + maxIn + 3).map(function () {\n return new List();\n });\n var zeroIdx = maxIn + 1;\n\n _.forEach(fasGraph.nodes(), function (v) {\n assignBucket(buckets, zeroIdx, fasGraph.node(v));\n });\n\n return { graph: fasGraph, buckets: buckets, zeroIdx: zeroIdx };\n}\n\nfunction assignBucket(buckets, zeroIdx, entry) {\n if (!entry.out) {\n buckets[0].enqueue(entry);\n } else if (!entry['in']) {\n buckets[buckets.length - 1].enqueue(entry);\n } else {\n buckets[entry.out - entry['in'] + zeroIdx].enqueue(entry);\n }\n}\n", "import * as _ from 'lodash-es';\nimport { greedyFAS } from './greedy-fas.js';\n\nexport { run, undo };\n\nfunction run(g) {\n var fas = g.graph().acyclicer === 'greedy' ? greedyFAS(g, weightFn(g)) : dfsFAS(g);\n _.forEach(fas, function (e) {\n var label = g.edge(e);\n g.removeEdge(e);\n label.forwardName = e.name;\n label.reversed = true;\n g.setEdge(e.w, e.v, label, _.uniqueId('rev'));\n });\n\n function weightFn(g) {\n return function (e) {\n return g.edge(e).weight;\n };\n }\n}\n\nfunction dfsFAS(g) {\n var fas = [];\n var stack = {};\n var visited = {};\n\n function dfs(v) {\n if (_.has(visited, v)) {\n return;\n }\n visited[v] = true;\n stack[v] = true;\n _.forEach(g.outEdges(v), function (e) {\n if (_.has(stack, e.w)) {\n fas.push(e);\n } else {\n dfs(e.w);\n }\n });\n delete stack[v];\n }\n\n _.forEach(g.nodes(), dfs);\n return fas;\n}\n\nfunction undo(g) {\n _.forEach(g.edges(), function (e) {\n var label = g.edge(e);\n if (label.reversed) {\n g.removeEdge(e);\n\n var forwardName = label.forwardName;\n delete label.reversed;\n delete label.forwardName;\n g.setEdge(e.w, e.v, label, forwardName);\n }\n });\n}\n", "import * as _ from 'lodash-es';\nimport * as util from './util.js';\n\nexport { run, undo };\n\n/*\n * Breaks any long edges in the graph into short segments that span 1 layer\n * each. This operation is undoable with the denormalize function.\n *\n * Pre-conditions:\n *\n * 1. The input graph is a DAG.\n * 2. Each node in the graph has a \"rank\" property.\n *\n * Post-condition:\n *\n * 1. All edges in the graph have a length of 1.\n * 2. Dummy nodes are added where edges have been split into segments.\n * 3. The graph is augmented with a \"dummyChains\" attribute which contains\n * the first dummy in each chain of dummy nodes produced.\n */\nfunction run(g) {\n g.graph().dummyChains = [];\n _.forEach(g.edges(), function (edge) {\n normalizeEdge(g, edge);\n });\n}\n\nfunction normalizeEdge(g, e) {\n var v = e.v;\n var vRank = g.node(v).rank;\n var w = e.w;\n var wRank = g.node(w).rank;\n var name = e.name;\n var edgeLabel = g.edge(e);\n var labelRank = edgeLabel.labelRank;\n\n if (wRank === vRank + 1) return;\n\n g.removeEdge(e);\n\n var dummy, attrs, i;\n for (i = 0, ++vRank; vRank < wRank; ++i, ++vRank) {\n edgeLabel.points = [];\n attrs = {\n width: 0,\n height: 0,\n edgeLabel: edgeLabel,\n edgeObj: e,\n rank: vRank,\n };\n dummy = util.addDummyNode(g, 'edge', attrs, '_d');\n if (vRank === labelRank) {\n attrs.width = edgeLabel.width;\n attrs.height = edgeLabel.height;\n // @ts-expect-error\n attrs.dummy = 'edge-label';\n // @ts-expect-error\n attrs.labelpos = edgeLabel.labelpos;\n }\n g.setEdge(v, dummy, { weight: edgeLabel.weight }, name);\n if (i === 0) {\n g.graph().dummyChains.push(dummy);\n }\n v = dummy;\n }\n\n g.setEdge(v, w, { weight: edgeLabel.weight }, name);\n}\n\nfunction undo(g) {\n _.forEach(g.graph().dummyChains, function (v) {\n var node = g.node(v);\n var origLabel = node.edgeLabel;\n var w;\n g.setEdge(node.edgeObj, origLabel);\n while (node.dummy) {\n w = g.successors(v)[0];\n g.removeNode(v);\n origLabel.points.push({ x: node.x, y: node.y });\n if (node.dummy === 'edge-label') {\n origLabel.x = node.x;\n origLabel.y = node.y;\n origLabel.width = node.width;\n origLabel.height = node.height;\n }\n v = w;\n node = g.node(v);\n }\n });\n}\n", "import * as _ from 'lodash-es';\n\nexport { longestPath, slack };\n\n/*\n * Initializes ranks for the input graph using the longest path algorithm. This\n * algorithm scales well and is fast in practice, it yields rather poor\n * solutions. Nodes are pushed to the lowest layer possible, leaving the bottom\n * ranks wide and leaving edges longer than necessary. However, due to its\n * speed, this algorithm is good for getting an initial ranking that can be fed\n * into other algorithms.\n *\n * This algorithm does not normalize layers because it will be used by other\n * algorithms in most cases. If using this algorithm directly, be sure to\n * run normalize at the end.\n *\n * Pre-conditions:\n *\n * 1. Input graph is a DAG.\n * 2. Input graph node labels can be assigned properties.\n *\n * Post-conditions:\n *\n * 1. Each node will be assign an (unnormalized) \"rank\" property.\n */\nfunction longestPath(g) {\n var visited = {};\n\n function dfs(v) {\n var label = g.node(v);\n if (_.has(visited, v)) {\n return label.rank;\n }\n visited[v] = true;\n\n var rank = _.min(\n _.map(g.outEdges(v), function (e) {\n return dfs(e.w) - g.edge(e).minlen;\n })\n );\n\n if (\n rank === Number.POSITIVE_INFINITY || // return value of _.map([]) for Lodash 3\n rank === undefined || // return value of _.map([]) for Lodash 4\n rank === null\n ) {\n // return value of _.map([null])\n rank = 0;\n }\n\n return (label.rank = rank);\n }\n\n _.forEach(g.sources(), dfs);\n}\n\n/*\n * Returns the amount of slack for the given edge. The slack is defined as the\n * difference between the length of the edge and its minimum length.\n */\nfunction slack(g, e) {\n return g.node(e.w).rank - g.node(e.v).rank - g.edge(e).minlen;\n}\n", "import * as _ from 'lodash-es';\nimport { Graph } from '../../graphlib/index.js';\nimport { slack } from './util.js';\n\nexport { feasibleTree };\n\n/*\n * Constructs a spanning tree with tight edges and adjusted the input node's\n * ranks to achieve this. A tight edge is one that is has a length that matches\n * its \"minlen\" attribute.\n *\n * The basic structure for this function is derived from Gansner, et al., \"A\n * Technique for Drawing Directed Graphs.\"\n *\n * Pre-conditions:\n *\n * 1. Graph must be a DAG.\n * 2. Graph must be connected.\n * 3. Graph must have at least one node.\n * 5. Graph nodes must have been previously assigned a \"rank\" property that\n * respects the \"minlen\" property of incident edges.\n * 6. Graph edges must have a \"minlen\" property.\n *\n * Post-conditions:\n *\n * - Graph nodes will have their rank adjusted to ensure that all edges are\n * tight.\n *\n * Returns a tree (undirected graph) that is constructed using only \"tight\"\n * edges.\n */\nfunction feasibleTree(g) {\n var t = new Graph({ directed: false });\n\n // Choose arbitrary node from which to start our tree\n var start = g.nodes()[0];\n var size = g.nodeCount();\n t.setNode(start, {});\n\n var edge, delta;\n while (tightTree(t, g) < size) {\n edge = findMinSlackEdge(t, g);\n delta = t.hasNode(edge.v) ? slack(g, edge) : -slack(g, edge);\n shiftRanks(t, g, delta);\n }\n\n return t;\n}\n\n/*\n * Finds a maximal tree of tight edges and returns the number of nodes in the\n * tree.\n */\nfunction tightTree(t, g) {\n function dfs(v) {\n _.forEach(g.nodeEdges(v), function (e) {\n var edgeV = e.v,\n w = v === edgeV ? e.w : edgeV;\n if (!t.hasNode(w) && !slack(g, e)) {\n t.setNode(w, {});\n t.setEdge(v, w, {});\n dfs(w);\n }\n });\n }\n\n _.forEach(t.nodes(), dfs);\n return t.nodeCount();\n}\n\n/*\n * Finds the edge with the smallest slack that is incident on tree and returns\n * it.\n */\nfunction findMinSlackEdge(t, g) {\n return _.minBy(g.edges(), function (e) {\n if (t.hasNode(e.v) !== t.hasNode(e.w)) {\n return slack(g, e);\n }\n });\n}\n\nfunction shiftRanks(t, g, delta) {\n _.forEach(t.nodes(), function (v) {\n g.node(v).rank += delta;\n });\n}\n", "import * as _ from 'lodash-es';\nimport { PriorityQueue } from '../data/priority-queue.js';\n\nexport { dijkstra };\n\nvar DEFAULT_WEIGHT_FUNC = _.constant(1);\n\nfunction dijkstra(g, source, weightFn, edgeFn) {\n return runDijkstra(\n g,\n String(source),\n weightFn || DEFAULT_WEIGHT_FUNC,\n edgeFn ||\n function (v) {\n return g.outEdges(v);\n }\n );\n}\n\nfunction runDijkstra(g, source, weightFn, edgeFn) {\n var results = {};\n var pq = new PriorityQueue();\n var v, vEntry;\n\n var updateNeighbors = function (edge) {\n var w = edge.v !== v ? edge.v : edge.w;\n var wEntry = results[w];\n var weight = weightFn(edge);\n var distance = vEntry.distance + weight;\n\n if (weight < 0) {\n throw new Error(\n 'dijkstra does not allow negative edge weights. ' +\n 'Bad edge: ' +\n edge +\n ' Weight: ' +\n weight\n );\n }\n\n if (distance < wEntry.distance) {\n wEntry.distance = distance;\n wEntry.predecessor = v;\n pq.decrease(w, distance);\n }\n };\n\n g.nodes().forEach(function (v) {\n var distance = v === source ? 0 : Number.POSITIVE_INFINITY;\n results[v] = { distance: distance };\n pq.add(v, distance);\n });\n\n while (pq.size() > 0) {\n v = pq.removeMin();\n vEntry = results[v];\n if (vEntry.distance === Number.POSITIVE_INFINITY) {\n break;\n }\n\n edgeFn(v).forEach(updateNeighbors);\n }\n\n return results;\n}\n", "import * as _ from 'lodash-es';\n\nexport { floydWarshall };\n\nvar DEFAULT_WEIGHT_FUNC = _.constant(1);\n\nfunction floydWarshall(g, weightFn, edgeFn) {\n return runFloydWarshall(\n g,\n weightFn || DEFAULT_WEIGHT_FUNC,\n edgeFn ||\n function (v) {\n return g.outEdges(v);\n }\n );\n}\n\nfunction runFloydWarshall(g, weightFn, edgeFn) {\n var results = {};\n var nodes = g.nodes();\n\n nodes.forEach(function (v) {\n results[v] = {};\n results[v][v] = { distance: 0 };\n nodes.forEach(function (w) {\n if (v !== w) {\n results[v][w] = { distance: Number.POSITIVE_INFINITY };\n }\n });\n edgeFn(v).forEach(function (edge) {\n var w = edge.v === v ? edge.w : edge.v;\n var d = weightFn(edge);\n results[v][w] = { distance: d, predecessor: v };\n });\n });\n\n nodes.forEach(function (k) {\n var rowK = results[k];\n nodes.forEach(function (i) {\n var rowI = results[i];\n nodes.forEach(function (j) {\n var ik = rowI[k];\n var kj = rowK[j];\n var ij = rowI[j];\n var altDistance = ik.distance + kj.distance;\n if (altDistance < ij.distance) {\n ij.distance = altDistance;\n ij.predecessor = kj.predecessor;\n }\n });\n });\n });\n\n return results;\n}\n", "import * as _ from 'lodash-es';\n\nexport { topsort, CycleException };\n\ntopsort.CycleException = CycleException;\n\nfunction topsort(g) {\n var visited = {};\n var stack = {};\n var results = [];\n\n function visit(node) {\n if (_.has(stack, node)) {\n throw new CycleException();\n }\n\n if (!_.has(visited, node)) {\n stack[node] = true;\n visited[node] = true;\n _.each(g.predecessors(node), visit);\n delete stack[node];\n results.push(node);\n }\n }\n\n _.each(g.sinks(), visit);\n\n if (_.size(visited) !== g.nodeCount()) {\n throw new CycleException();\n }\n\n return results;\n}\n\nfunction CycleException() {}\nCycleException.prototype = new Error(); // must be an instance of Error to pass testing\n", "import * as _ from 'lodash-es';\n\nexport { dfs };\n\n/*\n * A helper that preforms a pre- or post-order traversal on the input graph\n * and returns the nodes in the order they were visited. If the graph is\n * undirected then this algorithm will navigate using neighbors. If the graph\n * is directed then this algorithm will navigate using successors.\n *\n * Order must be one of \"pre\" or \"post\".\n */\nfunction dfs(g, vs, order) {\n if (!_.isArray(vs)) {\n vs = [vs];\n }\n\n var navigation = (g.isDirected() ? g.successors : g.neighbors).bind(g);\n\n var acc = [];\n var visited = {};\n _.each(vs, function (v) {\n if (!g.hasNode(v)) {\n throw new Error('Graph does not have node: ' + v);\n }\n\n doDfs(g, v, order === 'post', visited, navigation, acc);\n });\n return acc;\n}\n\nfunction doDfs(g, v, postorder, visited, navigation, acc) {\n if (!_.has(visited, v)) {\n visited[v] = true;\n\n if (!postorder) {\n acc.push(v);\n }\n _.each(navigation(v), function (w) {\n doDfs(g, w, postorder, visited, navigation, acc);\n });\n if (postorder) {\n acc.push(v);\n }\n }\n}\n", "import { dfs } from './dfs.js';\n\nexport { postorder };\n\nfunction postorder(g, vs) {\n return dfs(g, vs, 'post');\n}\n", "import { dfs } from './dfs.js';\n\nexport { preorder };\n\nfunction preorder(g, vs) {\n return dfs(g, vs, 'pre');\n}\n", "import * as _ from 'lodash-es';\nimport * as alg from '../../graphlib/alg/index.js';\nimport { simplify } from '../util.js';\nimport { feasibleTree } from './feasible-tree.js';\nimport { longestPath, slack } from './util.js';\n\nexport { networkSimplex };\n\n// Expose some internals for testing purposes\nnetworkSimplex.initLowLimValues = initLowLimValues;\nnetworkSimplex.initCutValues = initCutValues;\nnetworkSimplex.calcCutValue = calcCutValue;\nnetworkSimplex.leaveEdge = leaveEdge;\nnetworkSimplex.enterEdge = enterEdge;\nnetworkSimplex.exchangeEdges = exchangeEdges;\n\n/*\n * The network simplex algorithm assigns ranks to each node in the input graph\n * and iteratively improves the ranking to reduce the length of edges.\n *\n * Preconditions:\n *\n * 1. The input graph must be a DAG.\n * 2. All nodes in the graph must have an object value.\n * 3. All edges in the graph must have \"minlen\" and \"weight\" attributes.\n *\n * Postconditions:\n *\n * 1. All nodes in the graph will have an assigned \"rank\" attribute that has\n * been optimized by the network simplex algorithm. Ranks start at 0.\n *\n *\n * A rough sketch of the algorithm is as follows:\n *\n * 1. Assign initial ranks to each node. We use the longest path algorithm,\n * which assigns ranks to the lowest position possible. In general this\n * leads to very wide bottom ranks and unnecessarily long edges.\n * 2. Construct a feasible tight tree. A tight tree is one such that all\n * edges in the tree have no slack (difference between length of edge\n * and minlen for the edge). This by itself greatly improves the assigned\n * rankings by shorting edges.\n * 3. Iteratively find edges that have negative cut values. Generally a\n * negative cut value indicates that the edge could be removed and a new\n * tree edge could be added to produce a more compact graph.\n *\n * Much of the algorithms here are derived from Gansner, et al., \"A Technique\n * for Drawing Directed Graphs.\" The structure of the file roughly follows the\n * structure of the overall algorithm.\n */\nfunction networkSimplex(g) {\n g = simplify(g);\n longestPath(g);\n var t = feasibleTree(g);\n initLowLimValues(t);\n initCutValues(t, g);\n\n var e, f;\n while ((e = leaveEdge(t))) {\n f = enterEdge(t, g, e);\n exchangeEdges(t, g, e, f);\n }\n}\n\n/*\n * Initializes cut values for all edges in the tree.\n */\nfunction initCutValues(t, g) {\n var vs = alg.postorder(t, t.nodes());\n vs = vs.slice(0, vs.length - 1);\n _.forEach(vs, function (v) {\n assignCutValue(t, g, v);\n });\n}\n\nfunction assignCutValue(t, g, child) {\n var childLab = t.node(child);\n var parent = childLab.parent;\n t.edge(child, parent).cutvalue = calcCutValue(t, g, child);\n}\n\n/*\n * Given the tight tree, its graph, and a child in the graph calculate and\n * return the cut value for the edge between the child and its parent.\n */\nfunction calcCutValue(t, g, child) {\n var childLab = t.node(child);\n var parent = childLab.parent;\n // True if the child is on the tail end of the edge in the directed graph\n var childIsTail = true;\n // The graph's view of the tree edge we're inspecting\n var graphEdge = g.edge(child, parent);\n // The accumulated cut value for the edge between this node and its parent\n var cutValue = 0;\n\n if (!graphEdge) {\n childIsTail = false;\n graphEdge = g.edge(parent, child);\n }\n\n cutValue = graphEdge.weight;\n\n _.forEach(g.nodeEdges(child), function (e) {\n var isOutEdge = e.v === child,\n other = isOutEdge ? e.w : e.v;\n\n if (other !== parent) {\n var pointsToHead = isOutEdge === childIsTail,\n otherWeight = g.edge(e).weight;\n\n cutValue += pointsToHead ? otherWeight : -otherWeight;\n if (isTreeEdge(t, child, other)) {\n var otherCutValue = t.edge(child, other).cutvalue;\n cutValue += pointsToHead ? -otherCutValue : otherCutValue;\n }\n }\n });\n\n return cutValue;\n}\n\nfunction initLowLimValues(tree, root) {\n if (arguments.length < 2) {\n root = tree.nodes()[0];\n }\n dfsAssignLowLim(tree, {}, 1, root);\n}\n\nfunction dfsAssignLowLim(tree, visited, nextLim, v, parent) {\n var low = nextLim;\n var label = tree.node(v);\n\n visited[v] = true;\n _.forEach(tree.neighbors(v), function (w) {\n if (!_.has(visited, w)) {\n nextLim = dfsAssignLowLim(tree, visited, nextLim, w, v);\n }\n });\n\n label.low = low;\n label.lim = nextLim++;\n if (parent) {\n label.parent = parent;\n } else {\n // TODO should be able to remove this when we incrementally update low lim\n delete label.parent;\n }\n\n return nextLim;\n}\n\nfunction leaveEdge(tree) {\n return _.find(tree.edges(), function (e) {\n return tree.edge(e).cutvalue < 0;\n });\n}\n\nfunction enterEdge(t, g, edge) {\n var v = edge.v;\n var w = edge.w;\n\n // For the rest of this function we assume that v is the tail and w is the\n // head, so if we don't have this edge in the graph we should flip it to\n // match the correct orientation.\n if (!g.hasEdge(v, w)) {\n v = edge.w;\n w = edge.v;\n }\n\n var vLabel = t.node(v);\n var wLabel = t.node(w);\n var tailLabel = vLabel;\n var flip = false;\n\n // If the root is in the tail of the edge then we need to flip the logic that\n // checks for the head and tail nodes in the candidates function below.\n if (vLabel.lim > wLabel.lim) {\n tailLabel = wLabel;\n flip = true;\n }\n\n var candidates = _.filter(g.edges(), function (edge) {\n return (\n flip === isDescendant(t, t.node(edge.v), tailLabel) &&\n flip !== isDescendant(t, t.node(edge.w), tailLabel)\n );\n });\n\n return _.minBy(candidates, function (edge) {\n return slack(g, edge);\n });\n}\n\nfunction exchangeEdges(t, g, e, f) {\n var v = e.v;\n var w = e.w;\n t.removeEdge(v, w);\n t.setEdge(f.v, f.w, {});\n initLowLimValues(t);\n initCutValues(t, g);\n updateRanks(t, g);\n}\n\nfunction updateRanks(t, g) {\n var root = _.find(t.nodes(), function (v) {\n return !g.node(v).parent;\n });\n var vs = alg.preorder(t, root);\n vs = vs.slice(1);\n _.forEach(vs, function (v) {\n var parent = t.node(v).parent,\n edge = g.edge(v, parent),\n flipped = false;\n\n if (!edge) {\n edge = g.edge(parent, v);\n flipped = true;\n }\n\n g.node(v).rank = g.node(parent).rank + (flipped ? edge.minlen : -edge.minlen);\n });\n}\n\n/*\n * Returns true if the edge is in the tree.\n */\nfunction isTreeEdge(tree, u, v) {\n return tree.hasEdge(u, v);\n}\n\n/*\n * Returns true if the specified node is descendant of the root node per the\n * assigned low and lim attributes in the tree.\n */\nfunction isDescendant(tree, vLabel, rootLabel) {\n return rootLabel.low <= vLabel.lim && vLabel.lim <= rootLabel.lim;\n}\n", "import { feasibleTree } from './feasible-tree.js';\nimport { networkSimplex } from './network-simplex.js';\nimport { longestPath } from './util.js';\n\nexport { rank };\n\n/*\n * Assigns a rank to each node in the input graph that respects the \"minlen\"\n * constraint specified on edges between nodes.\n *\n * This basic structure is derived from Gansner, et al., \"A Technique for\n * Drawing Directed Graphs.\"\n *\n * Pre-conditions:\n *\n * 1. Graph must be a connected DAG\n * 2. Graph nodes must be objects\n * 3. Graph edges must have \"weight\" and \"minlen\" attributes\n *\n * Post-conditions:\n *\n * 1. Graph nodes will have a \"rank\" attribute based on the results of the\n * algorithm. Ranks can start at any index (including negative), we'll\n * fix them up later.\n */\nfunction rank(g) {\n switch (g.graph().ranker) {\n case 'network-simplex':\n networkSimplexRanker(g);\n break;\n case 'tight-tree':\n tightTreeRanker(g);\n break;\n case 'longest-path':\n longestPathRanker(g);\n break;\n default:\n networkSimplexRanker(g);\n }\n}\n\n// A fast and simple ranker, but results are far from optimal.\nvar longestPathRanker = longestPath;\n\nfunction tightTreeRanker(g) {\n longestPath(g);\n feasibleTree(g);\n}\n\nfunction networkSimplexRanker(g) {\n networkSimplex(g);\n}\n", "import * as _ from 'lodash-es';\nimport * as util from './util.js';\n\nexport { run, cleanup };\n\n/*\n * A nesting graph creates dummy nodes for the tops and bottoms of subgraphs,\n * adds appropriate edges to ensure that all cluster nodes are placed between\n * these boundries, and ensures that the graph is connected.\n *\n * In addition we ensure, through the use of the minlen property, that nodes\n * and subgraph border nodes to not end up on the same rank.\n *\n * Preconditions:\n *\n * 1. Input graph is a DAG\n * 2. Nodes in the input graph has a minlen attribute\n *\n * Postconditions:\n *\n * 1. Input graph is connected.\n * 2. Dummy nodes are added for the tops and bottoms of subgraphs.\n * 3. The minlen attribute for nodes is adjusted to ensure nodes do not\n * get placed on the same rank as subgraph border nodes.\n *\n * The nesting graph idea comes from Sander, \"Layout of Compound Directed\n * Graphs.\"\n */\nfunction run(g) {\n var root = util.addDummyNode(g, 'root', {}, '_root');\n var depths = treeDepths(g);\n var height = _.max(_.values(depths)) - 1; // Note: depths is an Object not an array\n var nodeSep = 2 * height + 1;\n\n g.graph().nestingRoot = root;\n\n // Multiply minlen by nodeSep to align nodes on non-border ranks.\n _.forEach(g.edges(), function (e) {\n g.edge(e).minlen *= nodeSep;\n });\n\n // Calculate a weight that is sufficient to keep subgraphs vertically compact\n var weight = sumWeights(g) + 1;\n\n // Create border nodes and link them up\n _.forEach(g.children(), function (child) {\n dfs(g, root, nodeSep, weight, height, depths, child);\n });\n\n // Save the multiplier for node layers for later removal of empty border\n // layers.\n g.graph().nodeRankFactor = nodeSep;\n}\n\nfunction dfs(g, root, nodeSep, weight, height, depths, v) {\n var children = g.children(v);\n if (!children.length) {\n if (v !== root) {\n g.setEdge(root, v, { weight: 0, minlen: nodeSep });\n }\n return;\n }\n\n var top = util.addBorderNode(g, '_bt');\n var bottom = util.addBorderNode(g, '_bb');\n var label = g.node(v);\n\n g.setParent(top, v);\n label.borderTop = top;\n g.setParent(bottom, v);\n label.borderBottom = bottom;\n\n _.forEach(children, function (child) {\n dfs(g, root, nodeSep, weight, height, depths, child);\n\n var childNode = g.node(child);\n var childTop = childNode.borderTop ? childNode.borderTop : child;\n var childBottom = childNode.borderBottom ? childNode.borderBottom : child;\n var thisWeight = childNode.borderTop ? weight : 2 * weight;\n var minlen = childTop !== childBottom ? 1 : height - depths[v] + 1;\n\n g.setEdge(top, childTop, {\n weight: thisWeight,\n minlen: minlen,\n nestingEdge: true,\n });\n\n g.setEdge(childBottom, bottom, {\n weight: thisWeight,\n minlen: minlen,\n nestingEdge: true,\n });\n });\n\n if (!g.parent(v)) {\n g.setEdge(root, top, { weight: 0, minlen: height + depths[v] });\n }\n}\n\nfunction treeDepths(g) {\n var depths = {};\n function dfs(v, depth) {\n var children = g.children(v);\n if (children && children.length) {\n _.forEach(children, function (child) {\n dfs(child, depth + 1);\n });\n }\n depths[v] = depth;\n }\n _.forEach(g.children(), function (v) {\n dfs(v, 1);\n });\n return depths;\n}\n\nfunction sumWeights(g) {\n return _.reduce(\n g.edges(),\n function (acc, e) {\n return acc + g.edge(e).weight;\n },\n 0\n );\n}\n\nfunction cleanup(g) {\n var graphLabel = g.graph();\n g.removeNode(graphLabel.nestingRoot);\n delete graphLabel.nestingRoot;\n _.forEach(g.edges(), function (e) {\n var edge = g.edge(e);\n if (edge.nestingEdge) {\n g.removeEdge(e);\n }\n });\n}\n", "import * as _ from 'lodash-es';\n\nexport { addSubgraphConstraints };\n\nfunction addSubgraphConstraints(g, cg, vs) {\n var prev = {},\n rootPrev;\n\n _.forEach(vs, function (v) {\n var child = g.parent(v),\n parent,\n prevChild;\n while (child) {\n parent = g.parent(child);\n if (parent) {\n prevChild = prev[parent];\n prev[parent] = child;\n } else {\n prevChild = rootPrev;\n rootPrev = child;\n }\n if (prevChild && prevChild !== child) {\n cg.setEdge(prevChild, child);\n return;\n }\n child = parent;\n }\n });\n\n /*\n function dfs(v) {\n var children = v ? g.children(v) : g.children();\n if (children.length) {\n var min = Number.POSITIVE_INFINITY,\n subgraphs = [];\n _.each(children, function(child) {\n var childMin = dfs(child);\n if (g.children(child).length) {\n subgraphs.push({ v: child, order: childMin });\n }\n min = Math.min(min, childMin);\n });\n _.reduce(_.sortBy(subgraphs, \"order\"), function(prev, curr) {\n cg.setEdge(prev.v, curr.v);\n return curr;\n });\n return min;\n }\n return g.node(v).order;\n }\n dfs(undefined);\n */\n}\n", "import * as _ from 'lodash-es';\nimport { Graph } from '../../graphlib/index.js';\n\nexport { buildLayerGraph };\n\n/*\n * Constructs a graph that can be used to sort a layer of nodes. The graph will\n * contain all base and subgraph nodes from the request layer in their original\n * hierarchy and any edges that are incident on these nodes and are of the type\n * requested by the \"relationship\" parameter.\n *\n * Nodes from the requested rank that do not have parents are assigned a root\n * node in the output graph, which is set in the root graph attribute. This\n * makes it easy to walk the hierarchy of movable nodes during ordering.\n *\n * Pre-conditions:\n *\n * 1. Input graph is a DAG\n * 2. Base nodes in the input graph have a rank attribute\n * 3. Subgraph nodes in the input graph has minRank and maxRank attributes\n * 4. Edges have an assigned weight\n *\n * Post-conditions:\n *\n * 1. Output graph has all nodes in the movable rank with preserved\n * hierarchy.\n * 2. Root nodes in the movable layer are made children of the node\n * indicated by the root attribute of the graph.\n * 3. Non-movable nodes incident on movable nodes, selected by the\n * relationship parameter, are included in the graph (without hierarchy).\n * 4. Edges incident on movable nodes, selected by the relationship\n * parameter, are added to the output graph.\n * 5. The weights for copied edges are aggregated as need, since the output\n * graph is not a multi-graph.\n */\nfunction buildLayerGraph(g, rank, relationship) {\n var root = createRootNode(g),\n result = new Graph({ compound: true })\n .setGraph({ root: root })\n .setDefaultNodeLabel(function (v) {\n return g.node(v);\n });\n\n _.forEach(g.nodes(), function (v) {\n var node = g.node(v),\n parent = g.parent(v);\n\n if (node.rank === rank || (node.minRank <= rank && rank <= node.maxRank)) {\n result.setNode(v);\n result.setParent(v, parent || root);\n\n // This assumes we have only short edges!\n _.forEach(g[relationship](v), function (e) {\n var u = e.v === v ? e.w : e.v,\n edge = result.edge(u, v),\n weight = !_.isUndefined(edge) ? edge.weight : 0;\n result.setEdge(u, v, { weight: g.edge(e).weight + weight });\n });\n\n if (_.has(node, 'minRank')) {\n result.setNode(v, {\n borderLeft: node.borderLeft[rank],\n borderRight: node.borderRight[rank],\n });\n }\n }\n });\n\n return result;\n}\n\nfunction createRootNode(g) {\n var v;\n while (g.hasNode((v = _.uniqueId('_root'))));\n return v;\n}\n", "import * as _ from 'lodash-es';\n\nexport { crossCount };\n\n/*\n * A function that takes a layering (an array of layers, each with an array of\n * ordererd nodes) and a graph and returns a weighted crossing count.\n *\n * Pre-conditions:\n *\n * 1. Input graph must be simple (not a multigraph), directed, and include\n * only simple edges.\n * 2. Edges in the input graph must have assigned weights.\n *\n * Post-conditions:\n *\n * 1. The graph and layering matrix are left unchanged.\n *\n * This algorithm is derived from Barth, et al., \"Bilayer Cross Counting.\"\n */\nfunction crossCount(g, layering) {\n var cc = 0;\n for (var i = 1; i < layering.length; ++i) {\n cc += twoLayerCrossCount(g, layering[i - 1], layering[i]);\n }\n return cc;\n}\n\nfunction twoLayerCrossCount(g, northLayer, southLayer) {\n // Sort all of the edges between the north and south layers by their position\n // in the north layer and then the south. Map these edges to the position of\n // their head in the south layer.\n var southPos = _.zipObject(\n southLayer,\n _.map(southLayer, function (v, i) {\n return i;\n })\n );\n var southEntries = _.flatten(\n _.map(northLayer, function (v) {\n return _.sortBy(\n _.map(g.outEdges(v), function (e) {\n return { pos: southPos[e.w], weight: g.edge(e).weight };\n }),\n 'pos'\n );\n })\n );\n\n // Build the accumulator tree\n var firstIndex = 1;\n while (firstIndex < southLayer.length) firstIndex <<= 1;\n var treeSize = 2 * firstIndex - 1;\n firstIndex -= 1;\n var tree = _.map(new Array(treeSize), function () {\n return 0;\n });\n\n // Calculate the weighted crossings\n var cc = 0;\n _.forEach(\n // @ts-expect-error\n southEntries.forEach(function (entry) {\n var index = entry.pos + firstIndex;\n tree[index] += entry.weight;\n var weightSum = 0;\n // @ts-expect-error\n while (index > 0) {\n // @ts-expect-error\n if (index % 2) {\n weightSum += tree[index + 1];\n }\n // @ts-expect-error\n index = (index - 1) >> 1;\n tree[index] += entry.weight;\n }\n cc += entry.weight * weightSum;\n })\n );\n\n return cc;\n}\n", "import * as _ from 'lodash-es';\n\nexport { initOrder };\n\n/*\n * Assigns an initial order value for each node by performing a DFS search\n * starting from nodes in the first rank. Nodes are assigned an order in their\n * rank as they are first visited.\n *\n * This approach comes from Gansner, et al., \"A Technique for Drawing Directed\n * Graphs.\"\n *\n * Returns a layering matrix with an array per layer and each layer sorted by\n * the order of its nodes.\n */\nfunction initOrder(g) {\n var visited = {};\n var simpleNodes = _.filter(g.nodes(), function (v) {\n return !g.children(v).length;\n });\n var maxRank = _.max(\n _.map(simpleNodes, function (v) {\n return g.node(v).rank;\n })\n );\n var layers = _.map(_.range(maxRank + 1), function () {\n return [];\n });\n\n function dfs(v) {\n if (_.has(visited, v)) return;\n visited[v] = true;\n var node = g.node(v);\n layers[node.rank].push(v);\n _.forEach(g.successors(v), dfs);\n }\n\n var orderedVs = _.sortBy(simpleNodes, function (v) {\n return g.node(v).rank;\n });\n _.forEach(orderedVs, dfs);\n\n return layers;\n}\n", "import * as _ from 'lodash-es';\n\nexport { barycenter };\n\nfunction barycenter(g, movable) {\n return _.map(movable, function (v) {\n var inV = g.inEdges(v);\n if (!inV.length) {\n return { v: v };\n } else {\n var result = _.reduce(\n inV,\n function (acc, e) {\n var edge = g.edge(e),\n nodeU = g.node(e.v);\n return {\n sum: acc.sum + edge.weight * nodeU.order,\n weight: acc.weight + edge.weight,\n };\n },\n { sum: 0, weight: 0 }\n );\n\n return {\n v: v,\n barycenter: result.sum / result.weight,\n weight: result.weight,\n };\n }\n });\n}\n", "import * as _ from 'lodash-es';\n\nexport { resolveConflicts };\n\n/*\n * Given a list of entries of the form {v, barycenter, weight} and a\n * constraint graph this function will resolve any conflicts between the\n * constraint graph and the barycenters for the entries. If the barycenters for\n * an entry would violate a constraint in the constraint graph then we coalesce\n * the nodes in the conflict into a new node that respects the contraint and\n * aggregates barycenter and weight information.\n *\n * This implementation is based on the description in Forster, \"A Fast and\n * Simple Hueristic for Constrained Two-Level Crossing Reduction,\" thought it\n * differs in some specific details.\n *\n * Pre-conditions:\n *\n * 1. Each entry has the form {v, barycenter, weight}, or if the node has\n * no barycenter, then {v}.\n *\n * Returns:\n *\n * A new list of entries of the form {vs, i, barycenter, weight}. The list\n * `vs` may either be a singleton or it may be an aggregation of nodes\n * ordered such that they do not violate constraints from the constraint\n * graph. The property `i` is the lowest original index of any of the\n * elements in `vs`.\n */\nfunction resolveConflicts(entries, cg) {\n var mappedEntries = {};\n _.forEach(entries, function (entry, i) {\n var tmp = (mappedEntries[entry.v] = {\n indegree: 0,\n in: [],\n out: [],\n vs: [entry.v],\n i: i,\n });\n if (!_.isUndefined(entry.barycenter)) {\n // @ts-expect-error\n tmp.barycenter = entry.barycenter;\n // @ts-expect-error\n tmp.weight = entry.weight;\n }\n });\n\n _.forEach(cg.edges(), function (e) {\n var entryV = mappedEntries[e.v];\n var entryW = mappedEntries[e.w];\n if (!_.isUndefined(entryV) && !_.isUndefined(entryW)) {\n entryW.indegree++;\n entryV.out.push(mappedEntries[e.w]);\n }\n });\n\n var sourceSet = _.filter(mappedEntries, function (entry) {\n // @ts-expect-error\n return !entry.indegree;\n });\n\n return doResolveConflicts(sourceSet);\n}\n\nfunction doResolveConflicts(sourceSet) {\n var entries = [];\n\n function handleIn(vEntry) {\n return function (uEntry) {\n if (uEntry.merged) {\n return;\n }\n if (\n _.isUndefined(uEntry.barycenter) ||\n _.isUndefined(vEntry.barycenter) ||\n uEntry.barycenter >= vEntry.barycenter\n ) {\n mergeEntries(vEntry, uEntry);\n }\n };\n }\n\n function handleOut(vEntry) {\n return function (wEntry) {\n wEntry['in'].push(vEntry);\n if (--wEntry.indegree === 0) {\n sourceSet.push(wEntry);\n }\n };\n }\n\n while (sourceSet.length) {\n var entry = sourceSet.pop();\n entries.push(entry);\n _.forEach(entry['in'].reverse(), handleIn(entry));\n _.forEach(entry.out, handleOut(entry));\n }\n\n return _.map(\n _.filter(entries, function (entry) {\n return !entry.merged;\n }),\n function (entry) {\n return _.pick(entry, ['vs', 'i', 'barycenter', 'weight']);\n }\n );\n}\n\nfunction mergeEntries(target, source) {\n var sum = 0;\n var weight = 0;\n\n if (target.weight) {\n sum += target.barycenter * target.weight;\n weight += target.weight;\n }\n\n if (source.weight) {\n sum += source.barycenter * source.weight;\n weight += source.weight;\n }\n\n target.vs = source.vs.concat(target.vs);\n target.barycenter = sum / weight;\n target.weight = weight;\n target.i = Math.min(source.i, target.i);\n source.merged = true;\n}\n", "import * as _ from 'lodash-es';\nimport * as util from '../util.js';\n\nexport { sort };\n\nfunction sort(entries, biasRight) {\n var parts = util.partition(entries, function (entry) {\n return _.has(entry, 'barycenter');\n });\n var sortable = parts.lhs,\n unsortable = _.sortBy(parts.rhs, function (entry) {\n return -entry.i;\n }),\n vs = [],\n sum = 0,\n weight = 0,\n vsIndex = 0;\n\n sortable.sort(compareWithBias(!!biasRight));\n\n vsIndex = consumeUnsortable(vs, unsortable, vsIndex);\n\n _.forEach(sortable, function (entry) {\n vsIndex += entry.vs.length;\n vs.push(entry.vs);\n sum += entry.barycenter * entry.weight;\n weight += entry.weight;\n vsIndex = consumeUnsortable(vs, unsortable, vsIndex);\n });\n\n var result = { vs: _.flatten(vs) };\n if (weight) {\n result.barycenter = sum / weight;\n result.weight = weight;\n }\n return result;\n}\n\nfunction consumeUnsortable(vs, unsortable, index) {\n var last;\n while (unsortable.length && (last = _.last(unsortable)).i <= index) {\n unsortable.pop();\n vs.push(last.vs);\n index++;\n }\n return index;\n}\n\nfunction compareWithBias(bias) {\n return function (entryV, entryW) {\n if (entryV.barycenter < entryW.barycenter) {\n return -1;\n } else if (entryV.barycenter > entryW.barycenter) {\n return 1;\n }\n\n return !bias ? entryV.i - entryW.i : entryW.i - entryV.i;\n };\n}\n", "import * as _ from 'lodash-es';\nimport { barycenter } from './barycenter.js';\nimport { resolveConflicts } from './resolve-conflicts.js';\nimport { sort } from './sort.js';\n\nexport { sortSubgraph };\n\nfunction sortSubgraph(g, v, cg, biasRight) {\n var movable = g.children(v);\n var node = g.node(v);\n var bl = node ? node.borderLeft : undefined;\n var br = node ? node.borderRight : undefined;\n var subgraphs = {};\n\n if (bl) {\n movable = _.filter(movable, function (w) {\n return w !== bl && w !== br;\n });\n }\n\n var barycenters = barycenter(g, movable);\n _.forEach(barycenters, function (entry) {\n if (g.children(entry.v).length) {\n var subgraphResult = sortSubgraph(g, entry.v, cg, biasRight);\n subgraphs[entry.v] = subgraphResult;\n if (_.has(subgraphResult, 'barycenter')) {\n mergeBarycenters(entry, subgraphResult);\n }\n }\n });\n\n var entries = resolveConflicts(barycenters, cg);\n expandSubgraphs(entries, subgraphs);\n\n var result = sort(entries, biasRight);\n\n if (bl) {\n result.vs = _.flatten([bl, result.vs, br]);\n if (g.predecessors(bl).length) {\n var blPred = g.node(g.predecessors(bl)[0]),\n brPred = g.node(g.predecessors(br)[0]);\n if (!_.has(result, 'barycenter')) {\n result.barycenter = 0;\n result.weight = 0;\n }\n result.barycenter =\n (result.barycenter * result.weight + blPred.order + brPred.order) / (result.weight + 2);\n result.weight += 2;\n }\n }\n\n return result;\n}\n\nfunction expandSubgraphs(entries, subgraphs) {\n _.forEach(entries, function (entry) {\n entry.vs = _.flatten(\n entry.vs.map(function (v) {\n if (subgraphs[v]) {\n return subgraphs[v].vs;\n }\n return v;\n })\n );\n });\n}\n\nfunction mergeBarycenters(target, other) {\n if (!_.isUndefined(target.barycenter)) {\n target.barycenter =\n (target.barycenter * target.weight + other.barycenter * other.weight) /\n (target.weight + other.weight);\n target.weight += other.weight;\n } else {\n target.barycenter = other.barycenter;\n target.weight = other.weight;\n }\n}\n", "import * as _ from 'lodash-es';\nimport { Graph } from '../../graphlib/index.js';\nimport * as util from '../util.js';\nimport { addSubgraphConstraints } from './add-subgraph-constraints.js';\nimport { buildLayerGraph } from './build-layer-graph.js';\nimport { crossCount } from './cross-count.js';\nimport { initOrder } from './init-order.js';\nimport { sortSubgraph } from './sort-subgraph.js';\n\nexport { order };\n\n/*\n * Applies heuristics to minimize edge crossings in the graph and sets the best\n * order solution as an order attribute on each node.\n *\n * Pre-conditions:\n *\n * 1. Graph must be DAG\n * 2. Graph nodes must be objects with a \"rank\" attribute\n * 3. Graph edges must have the \"weight\" attribute\n *\n * Post-conditions:\n *\n * 1. Graph nodes will have an \"order\" attribute based on the results of the\n * algorithm.\n */\nfunction order(g) {\n var maxRank = util.maxRank(g),\n downLayerGraphs = buildLayerGraphs(g, _.range(1, maxRank + 1), 'inEdges'),\n upLayerGraphs = buildLayerGraphs(g, _.range(maxRank - 1, -1, -1), 'outEdges');\n\n var layering = initOrder(g);\n assignOrder(g, layering);\n\n var bestCC = Number.POSITIVE_INFINITY,\n best;\n\n for (var i = 0, lastBest = 0; lastBest < 4; ++i, ++lastBest) {\n sweepLayerGraphs(i % 2 ? downLayerGraphs : upLayerGraphs, i % 4 >= 2);\n\n layering = util.buildLayerMatrix(g);\n var cc = crossCount(g, layering);\n if (cc < bestCC) {\n lastBest = 0;\n best = _.cloneDeep(layering);\n bestCC = cc;\n }\n }\n\n assignOrder(g, best);\n}\n\nfunction buildLayerGraphs(g, ranks, relationship) {\n return _.map(ranks, function (rank) {\n return buildLayerGraph(g, rank, relationship);\n });\n}\n\nfunction sweepLayerGraphs(layerGraphs, biasRight) {\n var cg = new Graph();\n _.forEach(layerGraphs, function (lg) {\n var root = lg.graph().root;\n var sorted = sortSubgraph(lg, root, cg, biasRight);\n _.forEach(sorted.vs, function (v, i) {\n lg.node(v).order = i;\n });\n addSubgraphConstraints(lg, cg, sorted.vs);\n });\n}\n\nfunction assignOrder(g, layering) {\n _.forEach(layering, function (layer) {\n _.forEach(layer, function (v, i) {\n g.node(v).order = i;\n });\n });\n}\n", "import * as _ from 'lodash-es';\n\nexport { parentDummyChains };\n\nfunction parentDummyChains(g) {\n var postorderNums = postorder(g);\n\n _.forEach(g.graph().dummyChains, function (v) {\n var node = g.node(v);\n var edgeObj = node.edgeObj;\n var pathData = findPath(g, postorderNums, edgeObj.v, edgeObj.w);\n var path = pathData.path;\n var lca = pathData.lca;\n var pathIdx = 0;\n var pathV = path[pathIdx];\n var ascending = true;\n\n while (v !== edgeObj.w) {\n node = g.node(v);\n\n if (ascending) {\n while ((pathV = path[pathIdx]) !== lca && g.node(pathV).maxRank < node.rank) {\n pathIdx++;\n }\n\n if (pathV === lca) {\n ascending = false;\n }\n }\n\n if (!ascending) {\n while (\n pathIdx < path.length - 1 &&\n g.node((pathV = path[pathIdx + 1])).minRank <= node.rank\n ) {\n pathIdx++;\n }\n pathV = path[pathIdx];\n }\n\n g.setParent(v, pathV);\n v = g.successors(v)[0];\n }\n });\n}\n\n// Find a path from v to w through the lowest common ancestor (LCA). Return the\n// full path and the LCA.\nfunction findPath(g, postorderNums, v, w) {\n var vPath = [];\n var wPath = [];\n var low = Math.min(postorderNums[v].low, postorderNums[w].low);\n var lim = Math.max(postorderNums[v].lim, postorderNums[w].lim);\n var parent;\n var lca;\n\n // Traverse up from v to find the LCA\n parent = v;\n do {\n parent = g.parent(parent);\n vPath.push(parent);\n } while (parent && (postorderNums[parent].low > low || lim > postorderNums[parent].lim));\n lca = parent;\n\n // Traverse from w to LCA\n parent = w;\n while ((parent = g.parent(parent)) !== lca) {\n wPath.push(parent);\n }\n\n return { path: vPath.concat(wPath.reverse()), lca: lca };\n}\n\nfunction postorder(g) {\n var result = {};\n var lim = 0;\n\n function dfs(v) {\n var low = lim;\n _.forEach(g.children(v), dfs);\n result[v] = { low: low, lim: lim++ };\n }\n _.forEach(g.children(), dfs);\n\n return result;\n}\n", "import * as _ from 'lodash-es';\nimport { Graph } from '../../graphlib/index.js';\nimport * as util from '../util.js';\n\n/*\n * This module provides coordinate assignment based on Brandes and K\u00F6pf, \"Fast\n * and Simple Horizontal Coordinate Assignment.\"\n */\n\nexport {\n positionX,\n findType1Conflicts,\n findType2Conflicts,\n addConflict,\n hasConflict,\n verticalAlignment,\n horizontalCompaction,\n alignCoordinates,\n findSmallestWidthAlignment,\n balance,\n};\n\n/*\n * Marks all edges in the graph with a type-1 conflict with the \"type1Conflict\"\n * property. A type-1 conflict is one where a non-inner segment crosses an\n * inner segment. An inner segment is an edge with both incident nodes marked\n * with the \"dummy\" property.\n *\n * This algorithm scans layer by layer, starting with the second, for type-1\n * conflicts between the current layer and the previous layer. For each layer\n * it scans the nodes from left to right until it reaches one that is incident\n * on an inner segment. It then scans predecessors to determine if they have\n * edges that cross that inner segment. At the end a final scan is done for all\n * nodes on the current rank to see if they cross the last visited inner\n * segment.\n *\n * This algorithm (safely) assumes that a dummy node will only be incident on a\n * single node in the layers being scanned.\n */\nfunction findType1Conflicts(g, layering) {\n var conflicts = {};\n\n function visitLayer(prevLayer, layer) {\n var // last visited node in the previous layer that is incident on an inner\n // segment.\n k0 = 0,\n // Tracks the last node in this layer scanned for crossings with a type-1\n // segment.\n scanPos = 0,\n prevLayerLength = prevLayer.length,\n lastNode = _.last(layer);\n\n _.forEach(layer, function (v, i) {\n var w = findOtherInnerSegmentNode(g, v),\n k1 = w ? g.node(w).order : prevLayerLength;\n\n if (w || v === lastNode) {\n _.forEach(layer.slice(scanPos, i + 1), function (scanNode) {\n _.forEach(g.predecessors(scanNode), function (u) {\n var uLabel = g.node(u),\n uPos = uLabel.order;\n if ((uPos < k0 || k1 < uPos) && !(uLabel.dummy && g.node(scanNode).dummy)) {\n addConflict(conflicts, u, scanNode);\n }\n });\n });\n // @ts-expect-error\n scanPos = i + 1;\n k0 = k1;\n }\n });\n\n return layer;\n }\n\n _.reduce(layering, visitLayer);\n return conflicts;\n}\n\nfunction findType2Conflicts(g, layering) {\n var conflicts = {};\n\n function scan(south, southPos, southEnd, prevNorthBorder, nextNorthBorder) {\n var v;\n _.forEach(_.range(southPos, southEnd), function (i) {\n v = south[i];\n if (g.node(v).dummy) {\n _.forEach(g.predecessors(v), function (u) {\n var uNode = g.node(u);\n if (uNode.dummy && (uNode.order < prevNorthBorder || uNode.order > nextNorthBorder)) {\n addConflict(conflicts, u, v);\n }\n });\n }\n });\n }\n\n function visitLayer(north, south) {\n var prevNorthPos = -1,\n nextNorthPos,\n southPos = 0;\n\n _.forEach(south, function (v, southLookahead) {\n if (g.node(v).dummy === 'border') {\n var predecessors = g.predecessors(v);\n if (predecessors.length) {\n nextNorthPos = g.node(predecessors[0]).order;\n scan(south, southPos, southLookahead, prevNorthPos, nextNorthPos);\n // @ts-expect-error\n southPos = southLookahead;\n prevNorthPos = nextNorthPos;\n }\n }\n scan(south, southPos, south.length, nextNorthPos, north.length);\n });\n\n return south;\n }\n\n _.reduce(layering, visitLayer);\n return conflicts;\n}\n\nfunction findOtherInnerSegmentNode(g, v) {\n if (g.node(v).dummy) {\n return _.find(g.predecessors(v), function (u) {\n return g.node(u).dummy;\n });\n }\n}\n\nfunction addConflict(conflicts, v, w) {\n if (v > w) {\n var tmp = v;\n v = w;\n w = tmp;\n }\n\n var conflictsV = conflicts[v];\n if (!conflictsV) {\n conflicts[v] = conflictsV = {};\n }\n conflictsV[w] = true;\n}\n\nfunction hasConflict(conflicts, v, w) {\n if (v > w) {\n var tmp = v;\n v = w;\n w = tmp;\n }\n return _.has(conflicts[v], w);\n}\n\n/*\n * Try to align nodes into vertical \"blocks\" where possible. This algorithm\n * attempts to align a node with one of its median neighbors. If the edge\n * connecting a neighbor is a type-1 conflict then we ignore that possibility.\n * If a previous node has already formed a block with a node after the node\n * we're trying to form a block with, we also ignore that possibility - our\n * blocks would be split in that scenario.\n */\nfunction verticalAlignment(g, layering, conflicts, neighborFn) {\n var root = {},\n align = {},\n pos = {};\n\n // We cache the position here based on the layering because the graph and\n // layering may be out of sync. The layering matrix is manipulated to\n // generate different extreme alignments.\n _.forEach(layering, function (layer) {\n _.forEach(layer, function (v, order) {\n root[v] = v;\n align[v] = v;\n pos[v] = order;\n });\n });\n\n _.forEach(layering, function (layer) {\n var prevIdx = -1;\n _.forEach(layer, function (v) {\n var ws = neighborFn(v);\n if (ws.length) {\n ws = _.sortBy(ws, function (w) {\n return pos[w];\n });\n var mp = (ws.length - 1) / 2;\n for (var i = Math.floor(mp), il = Math.ceil(mp); i <= il; ++i) {\n var w = ws[i];\n if (align[v] === v && prevIdx < pos[w] && !hasConflict(conflicts, v, w)) {\n align[w] = v;\n align[v] = root[v] = root[w];\n prevIdx = pos[w];\n }\n }\n }\n });\n });\n\n return { root: root, align: align };\n}\n\nfunction horizontalCompaction(g, layering, root, align, reverseSep) {\n // This portion of the algorithm differs from BK due to a number of problems.\n // Instead of their algorithm we construct a new block graph and do two\n // sweeps. The first sweep places blocks with the smallest possible\n // coordinates. The second sweep removes unused space by moving blocks to the\n // greatest coordinates without violating separation.\n var xs = {},\n blockG = buildBlockGraph(g, layering, root, reverseSep),\n borderType = reverseSep ? 'borderLeft' : 'borderRight';\n\n function iterate(setXsFunc, nextNodesFunc) {\n var stack = blockG.nodes();\n var elem = stack.pop();\n var visited = {};\n while (elem) {\n if (visited[elem]) {\n setXsFunc(elem);\n } else {\n visited[elem] = true;\n stack.push(elem);\n stack = stack.concat(nextNodesFunc(elem));\n }\n\n elem = stack.pop();\n }\n }\n\n // First pass, assign smallest coordinates\n function pass1(elem) {\n xs[elem] = blockG.inEdges(elem).reduce(function (acc, e) {\n return Math.max(acc, xs[e.v] + blockG.edge(e));\n }, 0);\n }\n\n // Second pass, assign greatest coordinates\n function pass2(elem) {\n var min = blockG.outEdges(elem).reduce(function (acc, e) {\n return Math.min(acc, xs[e.w] - blockG.edge(e));\n }, Number.POSITIVE_INFINITY);\n\n var node = g.node(elem);\n if (min !== Number.POSITIVE_INFINITY && node.borderType !== borderType) {\n xs[elem] = Math.max(xs[elem], min);\n }\n }\n\n iterate(pass1, blockG.predecessors.bind(blockG));\n iterate(pass2, blockG.successors.bind(blockG));\n\n // Assign x coordinates to all nodes\n _.forEach(align, function (v) {\n xs[v] = xs[root[v]];\n });\n\n return xs;\n}\n\nfunction buildBlockGraph(g, layering, root, reverseSep) {\n var blockGraph = new Graph(),\n graphLabel = g.graph(),\n sepFn = sep(graphLabel.nodesep, graphLabel.edgesep, reverseSep);\n\n _.forEach(layering, function (layer) {\n var u;\n _.forEach(layer, function (v) {\n var vRoot = root[v];\n blockGraph.setNode(vRoot);\n if (u) {\n var uRoot = root[u],\n prevMax = blockGraph.edge(uRoot, vRoot);\n blockGraph.setEdge(uRoot, vRoot, Math.max(sepFn(g, v, u), prevMax || 0));\n }\n u = v;\n });\n });\n\n return blockGraph;\n}\n\n/*\n * Returns the alignment that has the smallest width of the given alignments.\n */\nfunction findSmallestWidthAlignment(g, xss) {\n return _.minBy(_.values(xss), function (xs) {\n var max = Number.NEGATIVE_INFINITY;\n var min = Number.POSITIVE_INFINITY;\n\n _.forIn(xs, function (x, v) {\n var halfWidth = width(g, v) / 2;\n\n max = Math.max(x + halfWidth, max);\n min = Math.min(x - halfWidth, min);\n });\n\n return max - min;\n });\n}\n\n/*\n * Align the coordinates of each of the layout alignments such that\n * left-biased alignments have their minimum coordinate at the same point as\n * the minimum coordinate of the smallest width alignment and right-biased\n * alignments have their maximum coordinate at the same point as the maximum\n * coordinate of the smallest width alignment.\n */\nfunction alignCoordinates(xss, alignTo) {\n var alignToVals = _.values(alignTo),\n alignToMin = _.min(alignToVals),\n alignToMax = _.max(alignToVals);\n\n _.forEach(['u', 'd'], function (vert) {\n _.forEach(['l', 'r'], function (horiz) {\n var alignment = vert + horiz,\n xs = xss[alignment],\n delta;\n if (xs === alignTo) return;\n\n var xsVals = _.values(xs);\n delta = horiz === 'l' ? alignToMin - _.min(xsVals) : alignToMax - _.max(xsVals);\n\n if (delta) {\n xss[alignment] = _.mapValues(xs, function (x) {\n return x + delta;\n });\n }\n });\n });\n}\n\nfunction balance(xss, align) {\n return _.mapValues(xss.ul, function (ignore, v) {\n if (align) {\n return xss[align.toLowerCase()][v];\n } else {\n var xs = _.sortBy(_.map(xss, v));\n return (xs[1] + xs[2]) / 2;\n }\n });\n}\n\nfunction positionX(g) {\n var layering = util.buildLayerMatrix(g);\n var conflicts = _.merge(findType1Conflicts(g, layering), findType2Conflicts(g, layering));\n\n var xss = {};\n var adjustedLayering;\n _.forEach(['u', 'd'], function (vert) {\n adjustedLayering = vert === 'u' ? layering : _.values(layering).reverse();\n _.forEach(['l', 'r'], function (horiz) {\n if (horiz === 'r') {\n adjustedLayering = _.map(adjustedLayering, function (inner) {\n return _.values(inner).reverse();\n });\n }\n\n var neighborFn = (vert === 'u' ? g.predecessors : g.successors).bind(g);\n var align = verticalAlignment(g, adjustedLayering, conflicts, neighborFn);\n var xs = horizontalCompaction(g, adjustedLayering, align.root, align.align, horiz === 'r');\n if (horiz === 'r') {\n xs = _.mapValues(xs, function (x) {\n return -x;\n });\n }\n xss[vert + horiz] = xs;\n });\n });\n\n var smallestWidth = findSmallestWidthAlignment(g, xss);\n alignCoordinates(xss, smallestWidth);\n return balance(xss, g.graph().align);\n}\n\nfunction sep(nodeSep, edgeSep, reverseSep) {\n return function (g, v, w) {\n var vLabel = g.node(v);\n var wLabel = g.node(w);\n var sum = 0;\n var delta;\n\n sum += vLabel.width / 2;\n if (_.has(vLabel, 'labelpos')) {\n switch (vLabel.labelpos.toLowerCase()) {\n case 'l':\n delta = -vLabel.width / 2;\n break;\n case 'r':\n delta = vLabel.width / 2;\n break;\n }\n }\n if (delta) {\n sum += reverseSep ? delta : -delta;\n }\n delta = 0;\n\n sum += (vLabel.dummy ? edgeSep : nodeSep) / 2;\n sum += (wLabel.dummy ? edgeSep : nodeSep) / 2;\n\n sum += wLabel.width / 2;\n if (_.has(wLabel, 'labelpos')) {\n switch (wLabel.labelpos.toLowerCase()) {\n case 'l':\n delta = wLabel.width / 2;\n break;\n case 'r':\n delta = -wLabel.width / 2;\n break;\n }\n }\n if (delta) {\n sum += reverseSep ? delta : -delta;\n }\n delta = 0;\n\n return sum;\n };\n}\n\nfunction width(g, v) {\n return g.node(v).width;\n}\n", "import * as _ from 'lodash-es';\nimport * as util from '../util.js';\nimport { positionX } from './bk.js';\n\nexport { position };\n\nfunction position(g) {\n g = util.asNonCompoundGraph(g);\n\n positionY(g);\n _.forOwn(positionX(g), function (x, v) {\n g.node(v).x = x;\n });\n}\n\nfunction positionY(g) {\n var layering = util.buildLayerMatrix(g);\n var rankSep = g.graph().ranksep;\n var prevY = 0;\n _.forEach(layering, function (layer) {\n var maxHeight = _.max(\n _.map(layer, function (v) {\n return g.node(v).height;\n })\n );\n _.forEach(layer, function (v) {\n g.node(v).y = prevY + maxHeight / 2;\n });\n prevY += maxHeight + rankSep;\n });\n}\n", "import * as _ from 'lodash-es';\nimport { Graph } from '../graphlib/index.js';\nimport { addBorderSegments } from './add-border-segments.js';\nimport * as coordinateSystem from './coordinate-system.js';\nimport * as acyclic from './acyclic.js';\nimport * as normalize from './normalize.js';\nimport { rank } from './rank/index.js';\nimport * as nestingGraph from './nesting-graph.js';\nimport { order } from './order/index.js';\nimport { parentDummyChains } from './parent-dummy-chains.js';\nimport { position } from './position/index.js';\nimport * as util from './util.js';\n\nexport { layout };\n\nfunction layout(g, opts) {\n var time = opts && opts.debugTiming ? util.time : util.notime;\n time('layout', function () {\n var layoutGraph = time(' buildLayoutGraph', function () {\n return buildLayoutGraph(g);\n });\n time(' runLayout', function () {\n runLayout(layoutGraph, time);\n });\n time(' updateInputGraph', function () {\n updateInputGraph(g, layoutGraph);\n });\n });\n}\n\nfunction runLayout(g, time) {\n time(' makeSpaceForEdgeLabels', function () {\n makeSpaceForEdgeLabels(g);\n });\n time(' removeSelfEdges', function () {\n removeSelfEdges(g);\n });\n time(' acyclic', function () {\n acyclic.run(g);\n });\n time(' nestingGraph.run', function () {\n nestingGraph.run(g);\n });\n time(' rank', function () {\n rank(util.asNonCompoundGraph(g));\n });\n time(' injectEdgeLabelProxies', function () {\n injectEdgeLabelProxies(g);\n });\n time(' removeEmptyRanks', function () {\n util.removeEmptyRanks(g);\n });\n time(' nestingGraph.cleanup', function () {\n nestingGraph.cleanup(g);\n });\n time(' normalizeRanks', function () {\n util.normalizeRanks(g);\n });\n time(' assignRankMinMax', function () {\n assignRankMinMax(g);\n });\n time(' removeEdgeLabelProxies', function () {\n removeEdgeLabelProxies(g);\n });\n time(' normalize.run', function () {\n normalize.run(g);\n });\n time(' parentDummyChains', function () {\n parentDummyChains(g);\n });\n time(' addBorderSegments', function () {\n addBorderSegments(g);\n });\n time(' order', function () {\n order(g);\n });\n time(' insertSelfEdges', function () {\n insertSelfEdges(g);\n });\n time(' adjustCoordinateSystem', function () {\n coordinateSystem.adjust(g);\n });\n time(' position', function () {\n position(g);\n });\n time(' positionSelfEdges', function () {\n positionSelfEdges(g);\n });\n time(' removeBorderNodes', function () {\n removeBorderNodes(g);\n });\n time(' normalize.undo', function () {\n normalize.undo(g);\n });\n time(' fixupEdgeLabelCoords', function () {\n fixupEdgeLabelCoords(g);\n });\n time(' undoCoordinateSystem', function () {\n coordinateSystem.undo(g);\n });\n time(' translateGraph', function () {\n translateGraph(g);\n });\n time(' assignNodeIntersects', function () {\n assignNodeIntersects(g);\n });\n time(' reversePoints', function () {\n reversePointsForReversedEdges(g);\n });\n time(' acyclic.undo', function () {\n acyclic.undo(g);\n });\n}\n\n/*\n * Copies final layout information from the layout graph back to the input\n * graph. This process only copies whitelisted attributes from the layout graph\n * to the input graph, so it serves as a good place to determine what\n * attributes can influence layout.\n */\nfunction updateInputGraph(inputGraph, layoutGraph) {\n _.forEach(inputGraph.nodes(), function (v) {\n var inputLabel = inputGraph.node(v);\n var layoutLabel = layoutGraph.node(v);\n\n if (inputLabel) {\n inputLabel.x = layoutLabel.x;\n inputLabel.y = layoutLabel.y;\n\n if (layoutGraph.children(v).length) {\n inputLabel.width = layoutLabel.width;\n inputLabel.height = layoutLabel.height;\n }\n }\n });\n\n _.forEach(inputGraph.edges(), function (e) {\n var inputLabel = inputGraph.edge(e);\n var layoutLabel = layoutGraph.edge(e);\n\n inputLabel.points = layoutLabel.points;\n if (_.has(layoutLabel, 'x')) {\n inputLabel.x = layoutLabel.x;\n inputLabel.y = layoutLabel.y;\n }\n });\n\n inputGraph.graph().width = layoutGraph.graph().width;\n inputGraph.graph().height = layoutGraph.graph().height;\n}\n\nvar graphNumAttrs = ['nodesep', 'edgesep', 'ranksep', 'marginx', 'marginy'];\nvar graphDefaults = { ranksep: 50, edgesep: 20, nodesep: 50, rankdir: 'tb' };\nvar graphAttrs = ['acyclicer', 'ranker', 'rankdir', 'align'];\nvar nodeNumAttrs = ['width', 'height'];\nvar nodeDefaults = { width: 0, height: 0 };\nvar edgeNumAttrs = ['minlen', 'weight', 'width', 'height', 'labeloffset'];\nvar edgeDefaults = {\n minlen: 1,\n weight: 1,\n width: 0,\n height: 0,\n labeloffset: 10,\n labelpos: 'r',\n};\nvar edgeAttrs = ['labelpos'];\n\n/*\n * Constructs a new graph from the input graph, which can be used for layout.\n * This process copies only whitelisted attributes from the input graph to the\n * layout graph. Thus this function serves as a good place to determine what\n * attributes can influence layout.\n */\nfunction buildLayoutGraph(inputGraph) {\n var g = new Graph({ multigraph: true, compound: true });\n var graph = canonicalize(inputGraph.graph());\n\n g.setGraph(\n _.merge({}, graphDefaults, selectNumberAttrs(graph, graphNumAttrs), _.pick(graph, graphAttrs))\n );\n\n _.forEach(inputGraph.nodes(), function (v) {\n var node = canonicalize(inputGraph.node(v));\n g.setNode(v, _.defaults(selectNumberAttrs(node, nodeNumAttrs), nodeDefaults));\n g.setParent(v, inputGraph.parent(v));\n });\n\n _.forEach(inputGraph.edges(), function (e) {\n var edge = canonicalize(inputGraph.edge(e));\n g.setEdge(\n e,\n _.merge({}, edgeDefaults, selectNumberAttrs(edge, edgeNumAttrs), _.pick(edge, edgeAttrs))\n );\n });\n\n return g;\n}\n\n/*\n * This idea comes from the Gansner paper: to account for edge labels in our\n * layout we split each rank in half by doubling minlen and halving ranksep.\n * Then we can place labels at these mid-points between nodes.\n *\n * We also add some minimal padding to the width to push the label for the edge\n * away from the edge itself a bit.\n */\nfunction makeSpaceForEdgeLabels(g) {\n var graph = g.graph();\n graph.ranksep /= 2;\n _.forEach(g.edges(), function (e) {\n var edge = g.edge(e);\n edge.minlen *= 2;\n if (edge.labelpos.toLowerCase() !== 'c') {\n if (graph.rankdir === 'TB' || graph.rankdir === 'BT') {\n edge.width += edge.labeloffset;\n } else {\n edge.height += edge.labeloffset;\n }\n }\n });\n}\n\n/*\n * Creates temporary dummy nodes that capture the rank in which each edge's\n * label is going to, if it has one of non-zero width and height. We do this\n * so that we can safely remove empty ranks while preserving balance for the\n * label's position.\n */\nfunction injectEdgeLabelProxies(g) {\n _.forEach(g.edges(), function (e) {\n var edge = g.edge(e);\n if (edge.width && edge.height) {\n var v = g.node(e.v);\n var w = g.node(e.w);\n var label = { rank: (w.rank - v.rank) / 2 + v.rank, e: e };\n util.addDummyNode(g, 'edge-proxy', label, '_ep');\n }\n });\n}\n\nfunction assignRankMinMax(g) {\n var maxRank = 0;\n _.forEach(g.nodes(), function (v) {\n var node = g.node(v);\n if (node.borderTop) {\n node.minRank = g.node(node.borderTop).rank;\n node.maxRank = g.node(node.borderBottom).rank;\n // @ts-expect-error\n maxRank = _.max(maxRank, node.maxRank);\n }\n });\n g.graph().maxRank = maxRank;\n}\n\nfunction removeEdgeLabelProxies(g) {\n _.forEach(g.nodes(), function (v) {\n var node = g.node(v);\n if (node.dummy === 'edge-proxy') {\n g.edge(node.e).labelRank = node.rank;\n g.removeNode(v);\n }\n });\n}\n\nfunction translateGraph(g) {\n var minX = Number.POSITIVE_INFINITY;\n var maxX = 0;\n var minY = Number.POSITIVE_INFINITY;\n var maxY = 0;\n var graphLabel = g.graph();\n var marginX = graphLabel.marginx || 0;\n var marginY = graphLabel.marginy || 0;\n\n function getExtremes(attrs) {\n var x = attrs.x;\n var y = attrs.y;\n var w = attrs.width;\n var h = attrs.height;\n minX = Math.min(minX, x - w / 2);\n maxX = Math.max(maxX, x + w / 2);\n minY = Math.min(minY, y - h / 2);\n maxY = Math.max(maxY, y + h / 2);\n }\n\n _.forEach(g.nodes(), function (v) {\n getExtremes(g.node(v));\n });\n _.forEach(g.edges(), function (e) {\n var edge = g.edge(e);\n if (_.has(edge, 'x')) {\n getExtremes(edge);\n }\n });\n\n minX -= marginX;\n minY -= marginY;\n\n _.forEach(g.nodes(), function (v) {\n var node = g.node(v);\n node.x -= minX;\n node.y -= minY;\n });\n\n _.forEach(g.edges(), function (e) {\n var edge = g.edge(e);\n _.forEach(edge.points, function (p) {\n p.x -= minX;\n p.y -= minY;\n });\n if (_.has(edge, 'x')) {\n edge.x -= minX;\n }\n if (_.has(edge, 'y')) {\n edge.y -= minY;\n }\n });\n\n graphLabel.width = maxX - minX + marginX;\n graphLabel.height = maxY - minY + marginY;\n}\n\nfunction assignNodeIntersects(g) {\n _.forEach(g.edges(), function (e) {\n var edge = g.edge(e);\n var nodeV = g.node(e.v);\n var nodeW = g.node(e.w);\n var p1, p2;\n if (!edge.points) {\n edge.points = [];\n p1 = nodeW;\n p2 = nodeV;\n } else {\n p1 = edge.points[0];\n p2 = edge.points[edge.points.length - 1];\n }\n edge.points.unshift(util.intersectRect(nodeV, p1));\n edge.points.push(util.intersectRect(nodeW, p2));\n });\n}\n\nfunction fixupEdgeLabelCoords(g) {\n _.forEach(g.edges(), function (e) {\n var edge = g.edge(e);\n if (_.has(edge, 'x')) {\n if (edge.labelpos === 'l' || edge.labelpos === 'r') {\n edge.width -= edge.labeloffset;\n }\n switch (edge.labelpos) {\n case 'l':\n edge.x -= edge.width / 2 + edge.labeloffset;\n break;\n case 'r':\n edge.x += edge.width / 2 + edge.labeloffset;\n break;\n }\n }\n });\n}\n\nfunction reversePointsForReversedEdges(g) {\n _.forEach(g.edges(), function (e) {\n var edge = g.edge(e);\n if (edge.reversed) {\n edge.points.reverse();\n }\n });\n}\n\nfunction removeBorderNodes(g) {\n _.forEach(g.nodes(), function (v) {\n if (g.children(v).length) {\n var node = g.node(v);\n var t = g.node(node.borderTop);\n var b = g.node(node.borderBottom);\n var l = g.node(_.last(node.borderLeft));\n var r = g.node(_.last(node.borderRight));\n\n node.width = Math.abs(r.x - l.x);\n node.height = Math.abs(b.y - t.y);\n node.x = l.x + node.width / 2;\n node.y = t.y + node.height / 2;\n }\n });\n\n _.forEach(g.nodes(), function (v) {\n if (g.node(v).dummy === 'border') {\n g.removeNode(v);\n }\n });\n}\n\nfunction removeSelfEdges(g) {\n _.forEach(g.edges(), function (e) {\n if (e.v === e.w) {\n var node = g.node(e.v);\n if (!node.selfEdges) {\n node.selfEdges = [];\n }\n node.selfEdges.push({ e: e, label: g.edge(e) });\n g.removeEdge(e);\n }\n });\n}\n\nfunction insertSelfEdges(g) {\n var layers = util.buildLayerMatrix(g);\n _.forEach(layers, function (layer) {\n var orderShift = 0;\n _.forEach(layer, function (v, i) {\n var node = g.node(v);\n node.order = i + orderShift;\n _.forEach(node.selfEdges, function (selfEdge) {\n util.addDummyNode(\n g,\n 'selfedge',\n {\n width: selfEdge.label.width,\n height: selfEdge.label.height,\n rank: node.rank,\n order: i + ++orderShift,\n e: selfEdge.e,\n label: selfEdge.label,\n },\n '_se'\n );\n });\n delete node.selfEdges;\n });\n });\n}\n\nfunction positionSelfEdges(g) {\n _.forEach(g.nodes(), function (v) {\n var node = g.node(v);\n if (node.dummy === 'selfedge') {\n var selfNode = g.node(node.e.v);\n var x = selfNode.x + selfNode.width / 2;\n var y = selfNode.y;\n var dx = node.x - x;\n var dy = selfNode.height / 2;\n g.setEdge(node.e, node.label);\n g.removeNode(v);\n node.label.points = [\n { x: x + (2 * dx) / 3, y: y - dy },\n { x: x + (5 * dx) / 6, y: y - dy },\n { x: x + dx, y: y },\n { x: x + (5 * dx) / 6, y: y + dy },\n { x: x + (2 * dx) / 3, y: y + dy },\n ];\n node.label.x = node.x;\n node.label.y = node.y;\n }\n });\n}\n\nfunction selectNumberAttrs(obj, attrs) {\n return _.mapValues(_.pick(obj, attrs), Number);\n}\n\nfunction canonicalize(attrs) {\n var newAttrs = {};\n _.forEach(attrs, function (v, k) {\n newAttrs[k.toLowerCase()] = v;\n });\n return newAttrs;\n}\n"],
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}
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