/*
 * 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.
 */
import lodash from "lodash";
import { Edge, Graph } from "graphlib";

export function longestPath(g: Graph): void {
  const visited: Record<string, boolean> = {};

  function dfs(v: string): number {
    const label = g.node(v);
    if (lodash.has(visited, v)) {
      return label.rank;
    }
    visited[v] = true;

    let rank = Math.min(
      ...(g.outEdges(v) as Edge[]).map((e) => {
        return dfs(e.w) - g.edge(e).minlen;
      })
    );

    if (
      rank === Number.POSITIVE_INFINITY || // return value of _.map([]) for Lodash 3
      rank === undefined || // return value of _.map([]) for Lodash 4
      rank === null
    ) {
      // return value of _.map([null])
      rank = 0;
    }

    return (label.rank = rank);
  }

  g.sources().forEach(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: Graph, e: Edge): number {
  return g.node(e.w).rank - g.node(e.v).rank - g.edge(e).minlen;
}