graphlib — Functionality to operate with graphlike structures
Source code: Lib/graphlib.py

class graphlib.TopologicalSorter(graph=None)

Provides functionality to topologically sort a graph of hashable nodes.
A topological order is a linear ordering of the vertices in a graph such that for every directed edge u > v from vertex u to vertex v, vertex u comes before vertex v in the ordering. For instance, the vertices of the graph may represent tasks to be performed, and the edges may represent constraints that one task must be performed before another; in this example, a topological ordering is just a valid sequence for the tasks. A complete topological ordering is possible if and only if the graph has no directed cycles, that is, if it is a directed acyclic graph.
If the optional graph argument is provided it must be a dictionary representing a directed acyclic graph where the keys are nodes and the values are iterables of all predecessors of that node in the graph (the nodes that have edges that point to the value in the key). Additional nodes can be added to the graph using the
add()
method.In the general case, the steps required to perform the sorting of a given graph are as follows:
 Create an instance of the
TopologicalSorter
with an optional initial graph.  Add additional nodes to the graph.
 Call
prepare()
on the graph.  While
is_active()
isTrue
, iterate over the nodes returned byget_ready()
and process them. Calldone()
on each node as it finishes processing.
In case just an immediate sorting of the nodes in the graph is required and no parallelism is involved, the convenience method
TopologicalSorter.static_order()
can be used directly:pycon3Copy Code>>> graph = {"D": {"B", "C"}, "C": {"A"}, "B": {"A"}} >>> ts = TopologicalSorter(graph) >>> tuple(ts.static_order()) ('A', 'C', 'B', 'D')
The class is designed to easily support parallel processing of the nodes as they become ready. For instance:
PythonCopy Codetopological_sorter = TopologicalSorter() # Add nodes to 'topological_sorter'... topological_sorter.prepare() while topological_sorter.is_active(): for node in topological_sorter.get_ready(): # Worker threads or processes take nodes to work on off the # 'task_queue' queue. task_queue.put(node) # When the work for a node is done, workers put the node in # 'finalized_tasks_queue' so we can get more nodes to work on. # The definition of 'is_active()' guarantees that, at this point, at # least one node has been placed on 'task_queue' that hasn't yet # been passed to 'done()', so this blocking 'get()' must (eventually) # succeed. After calling 'done()', we loop back to call 'get_ready()' # again, so put newly freed nodes on 'task_queue' as soon as # logically possible. node = finalized_tasks_queue.get() topological_sorter.done(node)

add(node, *predecessors)

Add a new node and its predecessors to the graph. Both the node and all elements in predecessors must be hashable.
If called multiple times with the same node argument, the set of dependencies will be the union of all dependencies passed in.
It is possible to add a node with no dependencies (predecessors is not provided) or to provide a dependency twice. If a node that has not been provided before is included among predecessors it will be automatically added to the graph with no predecessors of its own.
Raises
ValueError
if called afterprepare()
.

prepare()

Mark the graph as finished and check for cycles in the graph. If any cycle is detected,
CycleError
will be raised, butget_ready()
can still be used to obtain as many nodes as possible until cycles block more progress. After a call to this function, the graph cannot be modified, and therefore no more nodes can be added usingadd()
.

is_active()

Returns
True
if more progress can be made andFalse
otherwise. Progress can be made if cycles do not block the resolution and either there are still nodes ready that haven’t yet been returned byTopologicalSorter.get_ready()
or the number of nodes markedTopologicalSorter.done()
is less than the number that have been returned byTopologicalSorter.get_ready()
.The
__bool__()
method of this class defers to this function, so instead of:PythonCopy Codeif ts.is_active(): ...
it is possible to simply do:
PythonCopy Codeif ts: ...
Raises
ValueError
if called without callingprepare()
previously.

done(*nodes)

Marks a set of nodes returned by
TopologicalSorter.get_ready()
as processed, unblocking any successor of each node in nodes for being returned in the future by a call toTopologicalSorter.get_ready()
.Raises
ValueError
if any node in nodes has already been marked as processed by a previous call to this method or if a node was not added to the graph by usingTopologicalSorter.add()
, if called without callingprepare()
or if node has not yet been returned byget_ready()
.

get_ready()

Returns a
tuple
with all the nodes that are ready. Initially it returns all nodes with no predecessors, and once those are marked as processed by callingTopologicalSorter.done()
, further calls will return all new nodes that have all their predecessors already processed. Once no more progress can be made, empty tuples are returned.Raises
ValueError
if called without callingprepare()
previously.

static_order()

Returns an iterable of nodes in a topological order. Using this method does not require to call
TopologicalSorter.prepare()
orTopologicalSorter.done()
. This method is equivalent to:PythonCopy Codedef static_order(self): self.prepare() while self.is_active(): node_group = self.get_ready() yield from node_group self.done(*node_group)
The particular order that is returned may depend on the specific order in which the items were inserted in the graph. For example:
pycon3Copy Code>>> ts = TopologicalSorter() >>> ts.add(3, 2, 1) >>> ts.add(1, 0) >>> print([*ts.static_order()]) [2, 0, 1, 3] >>> ts2 = TopologicalSorter() >>> ts2.add(1, 0) >>> ts2.add(3, 2, 1) >>> print([*ts2.static_order()]) [0, 2, 1, 3]
This is due to the fact that “0” and “2” are in the same level in the graph (they would have been returned in the same call to
get_ready()
) and the order between them is determined by the order of insertion.If any cycle is detected,
CycleError
will be raised.
New in version 3.9.
 Create an instance of the
Exceptions
The graphlib
module defines the following exception classes:

exception graphlib.CycleError

Subclass of
ValueError
raised byTopologicalSorter.prepare()
if cycles exist in the working graph. If multiple cycles exist, only one undefined choice among them will be reported and included in the exception.The detected cycle can be accessed via the second element in the
args
attribute of the exception instance and consists in a list of nodes, such that each node is, in the graph, an immediate predecessor of the next node in the list. In the reported list, the first and the last node will be the same, to make it clear that it is cyclic.
License
© 2001–2021 Python Software Foundation
Licensed under the PSF License.
https://docs.python.org/3.9/library/graphlib.html