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- import _ from 'lodash'
- /*
- * Initializes ranks for the input graph using the longest path algorithm. This
- * algorithm scales well and is fast in practice, it yields rather poor
- * solutions. Nodes are pushed to the lowest layer possible, leaving the bottom
- * ranks wide and leaving edges longer than necessary. However, due to its
- * speed, this algorithm is good for getting an initial ranking that can be fed
- * into other algorithms.
- *
- * This algorithm does not normalize layers because it will be used by other
- * algorithms in most cases. If using this algorithm directly, be sure to
- * run normalize at the end.
- *
- * Pre-conditions:
- *
- * 1. Input graph is a DAG.
- * 2. Input graph node labels can be assigned properties.
- *
- * Post-conditions:
- *
- * 1. Each node will be assign an (unnormalized) "rank" property.
- */
- export function longestPath (g) {
- const visited = {}
- function dfs (v) {
- const label = g.node(v)
- if (_.has(visited, v)) {
- return label.rank
- }
- visited[v] = true
- const rank = _.min(_.map(g.outEdges(v), function (e) {
- return dfs(e.w) - g.edge(e).minlen
- })) || 0
- return (label.rank = rank)
- }
- _.forEach(g.sources(), dfs)
- }
- /*
- * Returns the amount of slack for the given edge. The slack is defined as the
- * difference between the length of the edge and its minimum length.
- */
- export function slack (g, e) {
- return g.node(e.w).rank - g.node(e.v).rank - g.edge(e).minlen
- }
- export default {
- longestPath: longestPath,
- slack: slack
- }
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