# Source code for networkx.algorithms.dag

```
"""Algorithms for directed acyclic graphs (DAGs).
Note that most of these functions are only guaranteed to work for DAGs.
In general, these functions do not check for acyclic-ness, so it is up
to the user to check for that.
"""
import heapq
from collections import deque
from functools import partial
from itertools import chain, combinations, product, starmap
from math import gcd
import networkx as nx
from networkx.utils import arbitrary_element, not_implemented_for, pairwise
__all__ = [
"descendants",
"ancestors",
"topological_sort",
"lexicographical_topological_sort",
"all_topological_sorts",
"topological_generations",
"is_directed_acyclic_graph",
"is_aperiodic",
"transitive_closure",
"transitive_closure_dag",
"transitive_reduction",
"antichains",
"dag_longest_path",
"dag_longest_path_length",
"dag_to_branching",
"compute_v_structures",
]
chaini = chain.from_iterable
[docs]def descendants(G, source):
"""Returns all nodes reachable from `source` in `G`.
Parameters
----------
G : NetworkX Graph
source : node in `G`
Returns
-------
set()
The descendants of `source` in `G`
Raises
------
NetworkXError
If node `source` is not in `G`.
Examples
--------
>>> DG = nx.path_graph(5, create_using=nx.DiGraph)
>>> sorted(nx.descendants(DG, 2))
[3, 4]
The `source` node is not a descendant of itself, but can be included manually:
>>> sorted(nx.descendants(DG, 2) | {2})
[2, 3, 4]
See also
--------
ancestors
"""
return {child for parent, child in nx.bfs_edges(G, source)}
[docs]def ancestors(G, source):
"""Returns all nodes having a path to `source` in `G`.
Parameters
----------
G : NetworkX Graph
source : node in `G`
Returns
-------
set()
The ancestors of `source` in `G`
Raises
------
NetworkXError
If node `source` is not in `G`.
Examples
--------
>>> DG = nx.path_graph(5, create_using=nx.DiGraph)
>>> sorted(nx.ancestors(DG, 2))
[0, 1]
The `source` node is not an ancestor of itself, but can be included manually:
>>> sorted(nx.ancestors(DG, 2) | {2})
[0, 1, 2]
See also
--------
descendants
"""
return {child for parent, child in nx.bfs_edges(G, source, reverse=True)}
def has_cycle(G):
"""Decides whether the directed graph has a cycle."""
try:
# Feed the entire iterator into a zero-length deque.
deque(topological_sort(G), maxlen=0)
except nx.NetworkXUnfeasible:
return True
else:
return False
[docs]def is_directed_acyclic_graph(G):
"""Returns True if the graph `G` is a directed acyclic graph (DAG) or
False if not.
Parameters
----------
G : NetworkX graph
Returns
-------
bool
True if `G` is a DAG, False otherwise
Examples
--------
Undirected graph::
>>> G = nx.Graph([(1, 2), (2, 3)])
>>> nx.is_directed_acyclic_graph(G)
False
Directed graph with cycle::
>>> G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
>>> nx.is_directed_acyclic_graph(G)
False
Directed acyclic graph::
>>> G = nx.DiGraph([(1, 2), (2, 3)])
>>> nx.is_directed_acyclic_graph(G)
True
See also
--------
topological_sort
"""
return G.is_directed() and not has_cycle(G)
[docs]def topological_generations(G):
"""Stratifies a DAG into generations.
A topological generation is node collection in which ancestors of a node in each
generation are guaranteed to be in a previous generation, and any descendants of
a node are guaranteed to be in a following generation. Nodes are guaranteed to
be in the earliest possible generation that they can belong to.
Parameters
----------
G : NetworkX digraph
A directed acyclic graph (DAG)
Yields
------
sets of nodes
Yields sets of nodes representing each generation.
Raises
------
NetworkXError
Generations are defined for directed graphs only. If the graph
`G` is undirected, a :exc:`NetworkXError` is raised.
NetworkXUnfeasible
If `G` is not a directed acyclic graph (DAG) no topological generations
exist and a :exc:`NetworkXUnfeasible` exception is raised. This can also
be raised if `G` is changed while the returned iterator is being processed
RuntimeError
If `G` is changed while the returned iterator is being processed.
Examples
--------
>>> DG = nx.DiGraph([(2, 1), (3, 1)])
>>> [sorted(generation) for generation in nx.topological_generations(DG)]
[[2, 3], [1]]
Notes
-----
The generation in which a node resides can also be determined by taking the
max-path-distance from the node to the farthest leaf node. That value can
be obtained with this function using `enumerate(topological_generations(G))`.
See also
--------
topological_sort
"""
if not G.is_directed():
raise nx.NetworkXError("Topological sort not defined on undirected graphs.")
multigraph = G.is_multigraph()
indegree_map = {v: d for v, d in G.in_degree() if d > 0}
zero_indegree = [v for v, d in G.in_degree() if d == 0]
while zero_indegree:
this_generation = zero_indegree
zero_indegree = []
for node in this_generation:
if node not in G:
raise RuntimeError("Graph changed during iteration")
for child in G.neighbors(node):
try:
indegree_map[child] -= len(G[node][child]) if multigraph else 1
except KeyError as err:
raise RuntimeError("Graph changed during iteration") from err
if indegree_map[child] == 0:
zero_indegree.append(child)
del indegree_map[child]
yield this_generation
if indegree_map:
raise nx.NetworkXUnfeasible(
"Graph contains a cycle or graph changed during iteration"
)
[docs]def topological_sort(G):
"""Returns a generator of nodes in topologically sorted order.
A topological sort is a nonunique permutation of the nodes of a
directed graph such that an edge from u to v implies that u
appears before v in the topological sort order. This ordering is
valid only if the graph has no directed cycles.
Parameters
----------
G : NetworkX digraph
A directed acyclic graph (DAG)
Yields
------
nodes
Yields the nodes in topological sorted order.
Raises
------
NetworkXError
Topological sort is defined for directed graphs only. If the graph `G`
is undirected, a :exc:`NetworkXError` is raised.
NetworkXUnfeasible
If `G` is not a directed acyclic graph (DAG) no topological sort exists
and a :exc:`NetworkXUnfeasible` exception is raised. This can also be
raised if `G` is changed while the returned iterator is being processed
RuntimeError
If `G` is changed while the returned iterator is being processed.
Examples
--------
To get the reverse order of the topological sort:
>>> DG = nx.DiGraph([(1, 2), (2, 3)])
>>> list(reversed(list(nx.topological_sort(DG))))
[3, 2, 1]
If your DiGraph naturally has the edges representing tasks/inputs
and nodes representing people/processes that initiate tasks, then
topological_sort is not quite what you need. You will have to change
the tasks to nodes with dependence reflected by edges. The result is
a kind of topological sort of the edges. This can be done
with :func:`networkx.line_graph` as follows:
>>> list(nx.topological_sort(nx.line_graph(DG)))
[(1, 2), (2, 3)]
Notes
-----
This algorithm is based on a description and proof in
"Introduction to Algorithms: A Creative Approach" [1]_ .
See also
--------
is_directed_acyclic_graph, lexicographical_topological_sort
References
----------
.. [1] Manber, U. (1989).
*Introduction to Algorithms - A Creative Approach.* Addison-Wesley.
"""
for generation in nx.topological_generations(G):
yield from generation
[docs]def lexicographical_topological_sort(G, key=None):
"""Generate the nodes in the unique lexicographical topological sort order.
Generates a unique ordering of nodes by first sorting topologically (for which there are often
multiple valid orderings) and then additionally by sorting lexicographically.
A topological sort arranges the nodes of a directed graph so that the
upstream node of each directed edge precedes the downstream node.
It is always possible to find a solution for directed graphs that have no cycles.
There may be more than one valid solution.
Lexicographical sorting is just sorting alphabetically. It is used here to break ties in the
topological sort and to determine a single, unique ordering. This can be useful in comparing
sort results.
The lexicographical order can be customized by providing a function to the `key=` parameter.
The definition of the key function is the same as used in python's built-in `sort()`.
The function takes a single argument and returns a key to use for sorting purposes.
Lexicographical sorting can fail if the node names are un-sortable. See the example below.
The solution is to provide a function to the `key=` argument that returns sortable keys.
Parameters
----------
G : NetworkX digraph
A directed acyclic graph (DAG)
key : function, optional
A function of one argument that converts a node name to a comparison key.
It defines and resolves ambiguities in the sort order. Defaults to the identity function.
Yields
------
nodes
Yields the nodes of G in lexicographical topological sort order.
Raises
------
NetworkXError
Topological sort is defined for directed graphs only. If the graph `G`
is undirected, a :exc:`NetworkXError` is raised.
NetworkXUnfeasible
If `G` is not a directed acyclic graph (DAG) no topological sort exists
and a :exc:`NetworkXUnfeasible` exception is raised. This can also be
raised if `G` is changed while the returned iterator is being processed
RuntimeError
If `G` is changed while the returned iterator is being processed.
TypeError
Results from un-sortable node names.
Consider using `key=` parameter to resolve ambiguities in the sort order.
Examples
--------
>>> DG = nx.DiGraph([(2, 1), (2, 5), (1, 3), (1, 4), (5, 4)])
>>> list(nx.lexicographical_topological_sort(DG))
[2, 1, 3, 5, 4]
>>> list(nx.lexicographical_topological_sort(DG, key=lambda x: -x))
[2, 5, 1, 4, 3]
The sort will fail for any graph with integer and string nodes. Comparison of integer to strings
is not defined in python. Is 3 greater or less than 'red'?
>>> DG = nx.DiGraph([(1, 'red'), (3, 'red'), (1, 'green'), (2, 'blue')])
>>> list(nx.lexicographical_topological_sort(DG))
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'str' and 'int'
...
Incomparable nodes can be resolved using a `key` function. This example function
allows comparison of integers and strings by returning a tuple where the first
element is True for `str`, False otherwise. The second element is the node name.
This groups the strings and integers separately so they can be compared only among themselves.
>>> key = lambda node: (isinstance(node, str), node)
>>> list(nx.lexicographical_topological_sort(DG, key=key))
[1, 2, 3, 'blue', 'green', 'red']
Notes
-----
This algorithm is based on a description and proof in
"Introduction to Algorithms: A Creative Approach" [1]_ .
See also
--------
topological_sort
References
----------
.. [1] Manber, U. (1989).
*Introduction to Algorithms - A Creative Approach.* Addison-Wesley.
"""
if not G.is_directed():
msg = "Topological sort not defined on undirected graphs."
raise nx.NetworkXError(msg)
if key is None:
def key(node):
return node
nodeid_map = {n: i for i, n in enumerate(G)}
def create_tuple(node):
return key(node), nodeid_map[node], node
indegree_map = {v: d for v, d in G.in_degree() if d > 0}
# These nodes have zero indegree and ready to be returned.
zero_indegree = [create_tuple(v) for v, d in G.in_degree() if d == 0]
heapq.heapify(zero_indegree)
while zero_indegree:
_, _, node = heapq.heappop(zero_indegree)
if node not in G:
raise RuntimeError("Graph changed during iteration")
for _, child in G.edges(node):
try:
indegree_map[child] -= 1
except KeyError as err:
raise RuntimeError("Graph changed during iteration") from err
if indegree_map[child] == 0:
try:
heapq.heappush(zero_indegree, create_tuple(child))
except TypeError as err:
raise TypeError(
f"{err}\nConsider using `key=` parameter to resolve ambiguities in the sort order."
)
del indegree_map[child]
yield node
if indegree_map:
msg = "Graph contains a cycle or graph changed during iteration"
raise nx.NetworkXUnfeasible(msg)
[docs]@not_implemented_for("undirected")
def all_topological_sorts(G):
"""Returns a generator of _all_ topological sorts of the directed graph G.
A topological sort is a nonunique permutation of the nodes such that an
edge from u to v implies that u appears before v in the topological sort
order.
Parameters
----------
G : NetworkX DiGraph
A directed graph
Yields
------
topological_sort_order : list
a list of nodes in `G`, representing one of the topological sort orders
Raises
------
NetworkXNotImplemented
If `G` is not directed
NetworkXUnfeasible
If `G` is not acyclic
Examples
--------
To enumerate all topological sorts of directed graph:
>>> DG = nx.DiGraph([(1, 2), (2, 3), (2, 4)])
>>> list(nx.all_topological_sorts(DG))
[[1, 2, 4, 3], [1, 2, 3, 4]]
Notes
-----
Implements an iterative version of the algorithm given in [1].
References
----------
.. [1] Knuth, Donald E., Szwarcfiter, Jayme L. (1974).
"A Structured Program to Generate All Topological Sorting Arrangements"
Information Processing Letters, Volume 2, Issue 6, 1974, Pages 153-157,
ISSN 0020-0190,
https://doi.org/10.1016/0020-0190(74)90001-5.
Elsevier (North-Holland), Amsterdam
"""
if not G.is_directed():
raise nx.NetworkXError("Topological sort not defined on undirected graphs.")
# the names of count and D are chosen to match the global variables in [1]
# number of edges originating in a vertex v
count = dict(G.in_degree())
# vertices with indegree 0
D = deque([v for v, d in G.in_degree() if d == 0])
# stack of first value chosen at a position k in the topological sort
bases = []
current_sort = []
# do-while construct
while True:
assert all([count[v] == 0 for v in D])
if len(current_sort) == len(G):
yield list(current_sort)
# clean-up stack
while len(current_sort) > 0:
assert len(bases) == len(current_sort)
q = current_sort.pop()
# "restores" all edges (q, x)
# NOTE: it is important to iterate over edges instead
# of successors, so count is updated correctly in multigraphs
for _, j in G.out_edges(q):
count[j] += 1
assert count[j] >= 0
# remove entries from D
while len(D) > 0 and count[D[-1]] > 0:
D.pop()
# corresponds to a circular shift of the values in D
# if the first value chosen (the base) is in the first
# position of D again, we are done and need to consider the
# previous condition
D.appendleft(q)
if D[-1] == bases[-1]:
# all possible values have been chosen at current position
# remove corresponding marker
bases.pop()
else:
# there are still elements that have not been fixed
# at the current position in the topological sort
# stop removing elements, escape inner loop
break
else:
if len(D) == 0:
raise nx.NetworkXUnfeasible("Graph contains a cycle.")
# choose next node
q = D.pop()
# "erase" all edges (q, x)
# NOTE: it is important to iterate over edges instead
# of successors, so count is updated correctly in multigraphs
for _, j in G.out_edges(q):
count[j] -= 1
assert count[j] >= 0
if count[j] == 0:
D.append(j)
current_sort.append(q)
# base for current position might _not_ be fixed yet
if len(bases) < len(current_sort):
bases.append(q)
if len(bases) == 0:
break
[docs]def is_aperiodic(G):
"""Returns True if `G` is aperiodic.
A directed graph is aperiodic if there is no integer k > 1 that
divides the length of every cycle in the graph.
Parameters
----------
G : NetworkX DiGraph
A directed graph
Returns
-------
bool
True if the graph is aperiodic False otherwise
Raises
------
NetworkXError
If `G` is not directed
Examples
--------
A graph consisting of one cycle, the length of which is 2. Therefore ``k = 2``
divides the length of every cycle in the graph and thus the graph
is *not aperiodic*::
>>> DG = nx.DiGraph([(1, 2), (2, 1)])
>>> nx.is_aperiodic(DG)
False
A graph consisting of two cycles: one of length 2 and the other of length 3.
The cycle lengths are coprime, so there is no single value of k where ``k > 1``
that divides each cycle length and therefore the graph is *aperiodic*::
>>> DG = nx.DiGraph([(1, 2), (2, 3), (3, 1), (1, 4), (4, 1)])
>>> nx.is_aperiodic(DG)
True
A graph consisting of two cycles: one of length 2 and the other of length 4.
The lengths of the cycles share a common factor ``k = 2``, and therefore
the graph is *not aperiodic*::
>>> DG = nx.DiGraph([(1, 2), (2, 1), (3, 4), (4, 5), (5, 6), (6, 3)])
>>> nx.is_aperiodic(DG)
False
An acyclic graph, therefore the graph is *not aperiodic*::
>>> DG = nx.DiGraph([(1, 2), (2, 3)])
>>> nx.is_aperiodic(DG)
False
Notes
-----
This uses the method outlined in [1]_, which runs in $O(m)$ time
given $m$ edges in `G`. Note that a graph is not aperiodic if it is
acyclic as every integer trivial divides length 0 cycles.
References
----------
.. [1] Jarvis, J. P.; Shier, D. R. (1996),
"Graph-theoretic analysis of finite Markov chains,"
in Shier, D. R.; Wallenius, K. T., Applied Mathematical Modeling:
A Multidisciplinary Approach, CRC Press.
"""
if not G.is_directed():
raise nx.NetworkXError("is_aperiodic not defined for undirected graphs")
s = arbitrary_element(G)
levels = {s: 0}
this_level = [s]
g = 0
lev = 1
while this_level:
next_level = []
for u in this_level:
for v in G[u]:
if v in levels: # Non-Tree Edge
g = gcd(g, levels[u] - levels[v] + 1)
else: # Tree Edge
next_level.append(v)
levels[v] = lev
this_level = next_level
lev += 1
if len(levels) == len(G): # All nodes in tree
return g == 1
else:
return g == 1 and nx.is_aperiodic(G.subgraph(set(G) - set(levels)))
[docs]def transitive_closure(G, reflexive=False):
"""Returns transitive closure of a graph
The transitive closure of G = (V,E) is a graph G+ = (V,E+) such that
for all v, w in V there is an edge (v, w) in E+ if and only if there
is a path from v to w in G.
Handling of paths from v to v has some flexibility within this definition.
A reflexive transitive closure creates a self-loop for the path
from v to v of length 0. The usual transitive closure creates a
self-loop only if a cycle exists (a path from v to v with length > 0).
We also allow an option for no self-loops.
Parameters
----------
G : NetworkX Graph
A directed/undirected graph/multigraph.
reflexive : Bool or None, optional (default: False)
Determines when cycles create self-loops in the Transitive Closure.
If True, trivial cycles (length 0) create self-loops. The result
is a reflexive transitive closure of G.
If False (the default) non-trivial cycles create self-loops.
If None, self-loops are not created.
Returns
-------
NetworkX graph
The transitive closure of `G`
Raises
------
NetworkXError
If `reflexive` not in `{None, True, False}`
Examples
--------
The treatment of trivial (i.e. length 0) cycles is controlled by the
`reflexive` parameter.
Trivial (i.e. length 0) cycles do not create self-loops when
``reflexive=False`` (the default)::
>>> DG = nx.DiGraph([(1, 2), (2, 3)])
>>> TC = nx.transitive_closure(DG, reflexive=False)
>>> TC.edges()
OutEdgeView([(1, 2), (1, 3), (2, 3)])
However, nontrivial (i.e. length greater then 0) cycles create self-loops
when ``reflexive=False`` (the default)::
>>> DG = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
>>> TC = nx.transitive_closure(DG, reflexive=False)
>>> TC.edges()
OutEdgeView([(1, 2), (1, 3), (1, 1), (2, 3), (2, 1), (2, 2), (3, 1), (3, 2), (3, 3)])
Trivial cycles (length 0) create self-loops when ``reflexive=True``::
>>> DG = nx.DiGraph([(1, 2), (2, 3)])
>>> TC = nx.transitive_closure(DG, reflexive=True)
>>> TC.edges()
OutEdgeView([(1, 2), (1, 1), (1, 3), (2, 3), (2, 2), (3, 3)])
And the third option is not to create self-loops at all when ``reflexive=None``::
>>> DG = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
>>> TC = nx.transitive_closure(DG, reflexive=None)
>>> TC.edges()
OutEdgeView([(1, 2), (1, 3), (2, 3), (2, 1), (3, 1), (3, 2)])
References
----------
.. [1] https://www.ics.uci.edu/~eppstein/PADS/PartialOrder.py
"""
TC = G.copy()
if reflexive not in {None, True, False}:
raise nx.NetworkXError("Incorrect value for the parameter `reflexive`")
for v in G:
if reflexive is None:
TC.add_edges_from((v, u) for u in nx.descendants(G, v) if u not in TC[v])
elif reflexive is True:
TC.add_edges_from(
(v, u) for u in nx.descendants(G, v) | {v} if u not in TC[v]
)
elif reflexive is False:
TC.add_edges_from((v, e[1]) for e in nx.edge_bfs(G, v) if e[1] not in TC[v])
return TC
[docs]@not_implemented_for("undirected")
def transitive_closure_dag(G, topo_order=None):
"""Returns the transitive closure of a directed acyclic graph.
This function is faster than the function `transitive_closure`, but fails
if the graph has a cycle.
The transitive closure of G = (V,E) is a graph G+ = (V,E+) such that
for all v, w in V there is an edge (v, w) in E+ if and only if there
is a non-null path from v to w in G.
Parameters
----------
G : NetworkX DiGraph
A directed acyclic graph (DAG)
topo_order: list or tuple, optional
A topological order for G (if None, the function will compute one)
Returns
-------
NetworkX DiGraph
The transitive closure of `G`
Raises
------
NetworkXNotImplemented
If `G` is not directed
NetworkXUnfeasible
If `G` has a cycle
Examples
--------
>>> DG = nx.DiGraph([(1, 2), (2, 3)])
>>> TC = nx.transitive_closure_dag(DG)
>>> TC.edges()
OutEdgeView([(1, 2), (1, 3), (2, 3)])
Notes
-----
This algorithm is probably simple enough to be well-known but I didn't find
a mention in the literature.
"""
if topo_order is None:
topo_order = list(topological_sort(G))
TC = G.copy()
# idea: traverse vertices following a reverse topological order, connecting
# each vertex to its descendants at distance 2 as we go
for v in reversed(topo_order):
TC.add_edges_from((v, u) for u in nx.descendants_at_distance(TC, v, 2))
return TC
[docs]@not_implemented_for("undirected")
def transitive_reduction(G):
"""Returns transitive reduction of a directed graph
The transitive reduction of G = (V,E) is a graph G- = (V,E-) such that
for all v,w in V there is an edge (v,w) in E- if and only if (v,w) is
in E and there is no path from v to w in G with length greater than 1.
Parameters
----------
G : NetworkX DiGraph
A directed acyclic graph (DAG)
Returns
-------
NetworkX DiGraph
The transitive reduction of `G`
Raises
------
NetworkXError
If `G` is not a directed acyclic graph (DAG) transitive reduction is
not uniquely defined and a :exc:`NetworkXError` exception is raised.
Examples
--------
To perform transitive reduction on a DiGraph:
>>> DG = nx.DiGraph([(1, 2), (2, 3), (1, 3)])
>>> TR = nx.transitive_reduction(DG)
>>> list(TR.edges)
[(1, 2), (2, 3)]
To avoid unnecessary data copies, this implementation does not return a
DiGraph with node/edge data.
To perform transitive reduction on a DiGraph and transfer node/edge data:
>>> DG = nx.DiGraph()
>>> DG.add_edges_from([(1, 2), (2, 3), (1, 3)], color='red')
>>> TR = nx.transitive_reduction(DG)
>>> TR.add_nodes_from(DG.nodes(data=True))
>>> TR.add_edges_from((u, v, DG.edges[u, v]) for u, v in TR.edges)
>>> list(TR.edges(data=True))
[(1, 2, {'color': 'red'}), (2, 3, {'color': 'red'})]
References
----------
https://en.wikipedia.org/wiki/Transitive_reduction
"""
if not is_directed_acyclic_graph(G):
msg = "Directed Acyclic Graph required for transitive_reduction"
raise nx.NetworkXError(msg)
TR = nx.DiGraph()
TR.add_nodes_from(G.nodes())
descendants = {}
# count before removing set stored in descendants
check_count = dict(G.in_degree)
for u in G:
u_nbrs = set(G[u])
for v in G[u]:
if v in u_nbrs:
if v not in descendants:
descendants[v] = {y for x, y in nx.dfs_edges(G, v)}
u_nbrs -= descendants[v]
check_count[v] -= 1
if check_count[v] == 0:
del descendants[v]
TR.add_edges_from((u, v) for v in u_nbrs)
return TR
[docs]@not_implemented_for("undirected")
def antichains(G, topo_order=None):
"""Generates antichains from a directed acyclic graph (DAG).
An antichain is a subset of a partially ordered set such that any
two elements in the subset are incomparable.
Parameters
----------
G : NetworkX DiGraph
A directed acyclic graph (DAG)
topo_order: list or tuple, optional
A topological order for G (if None, the function will compute one)
Yields
------
antichain : list
a list of nodes in `G` representing an antichain
Raises
------
NetworkXNotImplemented
If `G` is not directed
NetworkXUnfeasible
If `G` contains a cycle
Examples
--------
>>> DG = nx.DiGraph([(1, 2), (1, 3)])
>>> list(nx.antichains(DG))
[[], [3], [2], [2, 3], [1]]
Notes
-----
This function was originally developed by Peter Jipsen and Franco Saliola
for the SAGE project. It's included in NetworkX with permission from the
authors. Original SAGE code at:
https://github.com/sagemath/sage/blob/master/src/sage/combinat/posets/hasse_diagram.py
References
----------
.. [1] Free Lattices, by R. Freese, J. Jezek and J. B. Nation,
AMS, Vol 42, 1995, p. 226.
"""
if topo_order is None:
topo_order = list(nx.topological_sort(G))
TC = nx.transitive_closure_dag(G, topo_order)
antichains_stacks = [([], list(reversed(topo_order)))]
while antichains_stacks:
(antichain, stack) = antichains_stacks.pop()
# Invariant:
# - the elements of antichain are independent
# - the elements of stack are independent from those of antichain
yield antichain
while stack:
x = stack.pop()
new_antichain = antichain + [x]
new_stack = [t for t in stack if not ((t in TC[x]) or (x in TC[t]))]
antichains_stacks.append((new_antichain, new_stack))
[docs]@not_implemented_for("undirected")
def dag_longest_path(G, weight="weight", default_weight=1, topo_order=None):
"""Returns the longest path in a directed acyclic graph (DAG).
If `G` has edges with `weight` attribute the edge data are used as
weight values.
Parameters
----------
G : NetworkX DiGraph
A directed acyclic graph (DAG)
weight : str, optional
Edge data key to use for weight
default_weight : int, optional
The weight of edges that do not have a weight attribute
topo_order: list or tuple, optional
A topological order for `G` (if None, the function will compute one)
Returns
-------
list
Longest path
Raises
------
NetworkXNotImplemented
If `G` is not directed
Examples
--------
>>> DG = nx.DiGraph([(0, 1, {'cost':1}), (1, 2, {'cost':1}), (0, 2, {'cost':42})])
>>> list(nx.all_simple_paths(DG, 0, 2))
[[0, 1, 2], [0, 2]]
>>> nx.dag_longest_path(DG)
[0, 1, 2]
>>> nx.dag_longest_path(DG, weight="cost")
[0, 2]
In the case where multiple valid topological orderings exist, `topo_order`
can be used to specify a specific ordering:
>>> DG = nx.DiGraph([(0, 1), (0, 2)])
>>> sorted(nx.all_topological_sorts(DG)) # Valid topological orderings
[[0, 1, 2], [0, 2, 1]]
>>> nx.dag_longest_path(DG, topo_order=[0, 1, 2])
[0, 1]
>>> nx.dag_longest_path(DG, topo_order=[0, 2, 1])
[0, 2]
See also
--------
dag_longest_path_length
"""
if not G:
return []
if topo_order is None:
topo_order = nx.topological_sort(G)
dist = {} # stores {v : (length, u)}
for v in topo_order:
us = [
(dist[u][0] + data.get(weight, default_weight), u)
for u, data in G.pred[v].items()
]
# Use the best predecessor if there is one and its distance is
# non-negative, otherwise terminate.
maxu = max(us, key=lambda x: x[0]) if us else (0, v)
dist[v] = maxu if maxu[0] >= 0 else (0, v)
u = None
v = max(dist, key=lambda x: dist[x][0])
path = []
while u != v:
path.append(v)
u = v
v = dist[v][1]
path.reverse()
return path
[docs]@not_implemented_for("undirected")
def dag_longest_path_length(G, weight="weight", default_weight=1):
"""Returns the longest path length in a DAG
Parameters
----------
G : NetworkX DiGraph
A directed acyclic graph (DAG)
weight : string, optional
Edge data key to use for weight
default_weight : int, optional
The weight of edges that do not have a weight attribute
Returns
-------
int
Longest path length
Raises
------
NetworkXNotImplemented
If `G` is not directed
Examples
--------
>>> DG = nx.DiGraph([(0, 1, {'cost':1}), (1, 2, {'cost':1}), (0, 2, {'cost':42})])
>>> list(nx.all_simple_paths(DG, 0, 2))
[[0, 1, 2], [0, 2]]
>>> nx.dag_longest_path_length(DG)
2
>>> nx.dag_longest_path_length(DG, weight="cost")
42
See also
--------
dag_longest_path
"""
path = nx.dag_longest_path(G, weight, default_weight)
path_length = 0
for (u, v) in pairwise(path):
path_length += G[u][v].get(weight, default_weight)
return path_length
def root_to_leaf_paths(G):
"""Yields root-to-leaf paths in a directed acyclic graph.
`G` must be a directed acyclic graph. If not, the behavior of this
function is undefined. A "root" in this graph is a node of in-degree
zero and a "leaf" a node of out-degree zero.
When invoked, this function iterates over each path from any root to
any leaf. A path is a list of nodes.
"""
roots = (v for v, d in G.in_degree() if d == 0)
leaves = (v for v, d in G.out_degree() if d == 0)
all_paths = partial(nx.all_simple_paths, G)
# TODO In Python 3, this would be better as `yield from ...`.
return chaini(starmap(all_paths, product(roots, leaves)))
[docs]@not_implemented_for("multigraph")
@not_implemented_for("undirected")
def dag_to_branching(G):
"""Returns a branching representing all (overlapping) paths from
root nodes to leaf nodes in the given directed acyclic graph.
As described in :mod:`networkx.algorithms.tree.recognition`, a
*branching* is a directed forest in which each node has at most one
parent. In other words, a branching is a disjoint union of
*arborescences*. For this function, each node of in-degree zero in
`G` becomes a root of one of the arborescences, and there will be
one leaf node for each distinct path from that root to a leaf node
in `G`.
Each node `v` in `G` with *k* parents becomes *k* distinct nodes in
the returned branching, one for each parent, and the sub-DAG rooted
at `v` is duplicated for each copy. The algorithm then recurses on
the children of each copy of `v`.
Parameters
----------
G : NetworkX graph
A directed acyclic graph.
Returns
-------
DiGraph
The branching in which there is a bijection between root-to-leaf
paths in `G` (in which multiple paths may share the same leaf)
and root-to-leaf paths in the branching (in which there is a
unique path from a root to a leaf).
Each node has an attribute 'source' whose value is the original
node to which this node corresponds. No other graph, node, or
edge attributes are copied into this new graph.
Raises
------
NetworkXNotImplemented
If `G` is not directed, or if `G` is a multigraph.
HasACycle
If `G` is not acyclic.
Examples
--------
To examine which nodes in the returned branching were produced by
which original node in the directed acyclic graph, we can collect
the mapping from source node to new nodes into a dictionary. For
example, consider the directed diamond graph::
>>> from collections import defaultdict
>>> from operator import itemgetter
>>>
>>> G = nx.DiGraph(nx.utils.pairwise("abd"))
>>> G.add_edges_from(nx.utils.pairwise("acd"))
>>> B = nx.dag_to_branching(G)
>>>
>>> sources = defaultdict(set)
>>> for v, source in B.nodes(data="source"):
... sources[source].add(v)
>>> len(sources["a"])
1
>>> len(sources["d"])
2
To copy node attributes from the original graph to the new graph,
you can use a dictionary like the one constructed in the above
example::
>>> for source, nodes in sources.items():
... for v in nodes:
... B.nodes[v].update(G.nodes[source])
Notes
-----
This function is not idempotent in the sense that the node labels in
the returned branching may be uniquely generated each time the
function is invoked. In fact, the node labels may not be integers;
in order to relabel the nodes to be more readable, you can use the
:func:`networkx.convert_node_labels_to_integers` function.
The current implementation of this function uses
:func:`networkx.prefix_tree`, so it is subject to the limitations of
that function.
"""
if has_cycle(G):
msg = "dag_to_branching is only defined for acyclic graphs"
raise nx.HasACycle(msg)
paths = root_to_leaf_paths(G)
B = nx.prefix_tree(paths)
# Remove the synthetic `root`(0) and `NIL`(-1) nodes from the tree
B.remove_node(0)
B.remove_node(-1)
return B
@not_implemented_for("undirected")
def compute_v_structures(G):
"""Iterate through the graph to compute all v-structures.
V-structures are triples in the directed graph where
two parent nodes point to the same child and the two parent nodes
are not adjacent.
Parameters
----------
G : graph
A networkx DiGraph.
Returns
-------
vstructs : iterator of tuples
The v structures within the graph. Each v structure is a 3-tuple with the
parent, collider, and other parent.
Notes
-----
https://en.wikipedia.org/wiki/Collider_(statistics)
"""
for collider, preds in G.pred.items():
for common_parents in combinations(preds, r=2):
# ensure that the colliders are the same
common_parents = sorted(common_parents)
yield (common_parents[0], collider, common_parents[1])
```