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ltsort

1.2.4 • Public • Published

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LTSort

With inspiration from https://github.com/eknkc/tsort and http://stackoverflow.com/a/5100904

Rationale

I needed a way to resolve dependencies, that is, to do a so-called topological sort, so I copied code from https://github.com/eknkc/tsort and modified it for my needs. Later I realized that doing a levelled (a.k.a. grouped) toposort would be very desirable, but I couldn't find any JavaScript / NodeJS module to do just that. I implemented the ideas given in http://stackoverflow.com/a/5100904, and added tests and documentation.

Example

Creating a graph and populating it with nodes and edges; the node labels appear in natural order (LTSORT.add g, a, b meaning that b depends on a having been done first):

LTSORT = require 'ltsort'
 
graph = LTSORT.new_graph loners: no
 
LTSORT.add graph'buy books',         'do some reading'
LTSORT.add graph'buy books',         'go home'
LTSORT.add graph'buy food',          'cook'
LTSORT.add graph'buy food',          'go home'
LTSORT.add graph'buy food',          'have a coffee'
LTSORT.add graph'cook',              'eat'
LTSORT.add graph'do some reading',   'go to exam'
LTSORT.add graph'eat',               'do some reading'
LTSORT.add graph'eat',               'go to exam'
LTSORT.add graph'fetch money',       'buy books'
LTSORT.add graph'fetch money',       'buy food'
LTSORT.add graph'go home',           'cook'
LTSORT.add graph'go to bank',        'fetch money'
LTSORT.add graph'have a coffee',     'go home'
 
tasks = LTSORT.group graph

tasks is now a list of lists:

[ [ 'go to bank' ],
  [ 'fetch money' ],
  [ 'buy books''buy food' ],
  [ 'have a coffee' ],
  [ 'go home' ],
  [ 'cook' ],
  [ 'eat' ],
  [ 'do some reading' ],
  [ 'go to exam' ]

which tells me that I'd have to cook before you eat, that I can only buy foods and books with some money on my hands, but that buying foods and books can happen in any order, and so on.

API

Creation

@new_graph settings

Create a new graph object. settings, if used, may be an object containing a single named item loners, which should be true or false; it is only relevant for LTSORT.find_root_nodes and LTSORT.group, for which see below. Alternatively, settings may be a graph itself, in which case a copy of that graph will be returned.

Population

@add graph, lhs, relation = null, rhs = null

Add a node or an edge to the graph. Possible forms are:

  • LTSORT.add graph, 'A'—add a node labelled A to the graph.

  • LTSORT.add graph, 'A', 'B'—add an edge to the graph that states that node A must precede node B. A and B will be added implicitly if not already present in the graph.

@populate graph, elements

Add all elements to the graph. elements should be a list of strings and pairs of strings; single strings will be registered as nodes and pairs of strings will be interpreted as precedent/consequent pairs. For example:

graph     = LTSORT.new_graph()
elements  = [
  [ 'A''X']
  [ 'B''X']
  'F'
  [ 'X''Y']
  [ 'X''Z']
  ]
LTSORT.populate graphelements

Deletion

@delete graph, name

Remove the node identified by name from graph. Currently, only nodes that have no precedents (i.e. root nodes, including unconnected ('lone') nodes) may be deleted. This is used by LTSORT.group, below.

Retrieval

@has_node graph, name

Return whether graph has a node labelled with name.

@has_nodes graph

Return whether graph has any nodes at all.

@is_lone_node graph, name

Return whether the node labelled name is a 'lone' node (i.e. one without precedents and without consequents; a node that is not part of any edge). Will throw an error if graph doesn't have a node labelled name.

@find_lone_nodes graph, root_nodes = null

Return a list of all lone nodes in the graph.

@find_root_nodes graph, loners = null

Return a list of all root nodes in the graph (i.e. those nodes that have no precedents / depend on nothing else in the graph). If loners is given and true, that list will include lone nodes; if it is given and false, that list will exclude lone nodes. If loners is not given, the graph's loners property will be used instead.

Sorting

@get_linearity graph

Linearity of a given dependency graph measures how well the dependency relations in a graph determine an ordering of its nodes. For a graph that defines a unique, single chain of antecedents and consequents, linearity will be 1; for a graph that defines only nodes and no dependency edges, linearity will be zero; for all other kind of graphs, linearity will be close to the inverse of the average group length.

Linerarity is directly related to how many groups (of mutually independent nodes) there are in the graph and how many nodes they contain; for example, here are the results of the LTSORT.group graph method and the respective linearity values:

[ [ 'A''B''C' ]
0
[ [ 'A''B''C''D''E' ]
0
[ [ 'A''B''C''D' ][ 'E' ]
0.25
[ [ 'A' ][ 'B' ][ 'C' ]
1

@linearize graph

Return a list with node labels, ordered such that all consequents come after their precedents. In other words, in a graph where edges represent dependencies and where a given task y depends on task x having been finished, its linearization will spell out one way in which to perform actions such that all prerequisites x come before any of their dependent tasks y. For example:

graph     = LTSORT.new_graph()
elements  = [
  [ 'A''X']
  [ 'B''X']
  'F'
  [ 'X''Y']
  [ 'X''Z']
  [ 'δ''B']
  [ 'Z''Ψ']
  [ 'Ψ''Ω']
  [ 'Z''Ω']
  [ 'β''A']
  [ 'α''β']
  ]
LTSORT.populate graphelements
tasks = LTSORT.linearize graph

tasks now equals

[ 'α''β''A''δ''B''X''F''Y''Z''Ψ''Ω' ]

(although the exact placement of some nodes such as F is not guaranteed). Going through the precedence rules given, we can ascertain this result is 'sufficiently good' to base a step-by-step procedure upon:

  • α ⇒ β was specified, and, indeed, α comes before β in the linearization.
  • A ⇒ X and B ⇒ X were specified, and, indeed, both A and B come before X.
  • Both Z and Y depend on X, and they indeed come after X.
  • δ ⇒ B ⇒ X was specified, and, indeed, δ, B and X appear in that order.
  • &c.

@group graph, loners = null

Like LTSORT.linearize, but return a list of lists instead. Using the same setup as shown above, but using LTSORT.group instead of LTSORT.linearize:

  tasks = LTSORT.group graph

we get:

  [ [ 'F' ],
    [ 'δ''α' ],
    [ 'B''β' ],
    [ 'A' ],
    [ 'X' ],
    [ 'Y''Z' ],
    [ 'Ψ' ],
    [ 'Ω' ]

Each element in the list represents a number of steps that may be performed in any order or in parallel (i.e. tasks that are independent of each other). Here we have created the graph with an (implicit) setting loners: true, which causes lone tasks to be singled out as the first (possibly empty) list; had we created the graph with loners: false (or called LTSORT.group graph, false), the first group of tasks would have become [ 'F', 'δ', 'α' ].

Observe that (1) the ordering of nodes within each group is not defined; it may or may not change when nodes and edges are added in a different order; (2) tasks appear as soon as possible in the listing, meaning that there's a chance that a given task could be accomplished later than indicated here. As with LTSORT.linearize, the result given is just one possible solution to the constraints given, and not necessarily the only one.

Install

npm i ltsort

DownloadsWeekly Downloads

28

Version

1.2.4

License

MIT

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235 kB

Total Files

12

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