mermaid/dist/chunks/mermaid.esm/chunk-IHYUGLNO.mjs.map

Markdown-ish syntax for generating flowcharts, mindmaps, sequence diagrams, class diagrams, gantt charts, git graphs and more.

{
  "version": 3,
  "sources": ["../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/util.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/add-border-segments.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/coordinate-system.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/data/list.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/greedy-fas.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/acyclic.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/normalize.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/rank/util.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/rank/feasible-tree.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/graphlib/alg/dijkstra.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/graphlib/alg/floyd-warshall.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/graphlib/alg/topsort.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/graphlib/alg/dfs.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/graphlib/alg/postorder.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/graphlib/alg/preorder.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/rank/network-simplex.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/rank/index.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/nesting-graph.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/order/add-subgraph-constraints.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/order/build-layer-graph.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/order/cross-count.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/order/init-order.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/order/barycenter.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/order/resolve-conflicts.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/order/sort.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/order/sort-subgraph.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/order/index.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/parent-dummy-chains.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/position/bk.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/position/index.js", "../../../../../node_modules/.pnpm/dagre-d3-es@7.0.11/node_modules/dagre-d3-es/src/dagre/layout.js"],
  "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 (Object.prototype.hasOwnProperty.call(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 (Object.prototype.hasOwnProperty.call(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 (Object.prototype.hasOwnProperty.call(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 (Object.prototype.hasOwnProperty.call(visited, v)) {\n      return;\n    }\n    visited[v] = true;\n    stack[v] = true;\n    _.forEach(g.outEdges(v), function (e) {\n      if (Object.prototype.hasOwnProperty.call(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", "/**\n * TypeScript type imports:\n *\n * @import { Graph } from '../graphlib/graph.js';\n */\nimport * 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\n/**\n * @param {Graph} g\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  /**\n   * @typedef {Object} Attrs\n   * @property {number} width\n   * @property {number} height\n   * @property {ReturnType<Graph[\"node\"]>} edgeLabel\n   * @property {any} edgeObj\n   * @property {ReturnType<Graph[\"node\"]>[\"rank\"]} rank\n   * @property {string} [dummy]\n   * @property {ReturnType<Graph[\"node\"]>[\"labelpos\"]} [labelpos]\n   */\n\n  /** @type {Attrs | undefined} */\n  var attrs = undefined;\n  var dummy, 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      attrs.dummy = 'edge-label';\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 (Object.prototype.hasOwnProperty.call(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 (Object.prototype.hasOwnProperty.call(stack, node)) {\n      throw new CycleException();\n    }\n\n    if (!Object.prototype.hasOwnProperty.call(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 (!Object.prototype.hasOwnProperty.call(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 (!Object.prototype.hasOwnProperty.call(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 (Object.prototype.hasOwnProperty.call(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\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 */\nexport function 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