```"""Basic algorithms for breadth-first searching the nodes of a graph."""
from collections import deque

import networkx as nx

__all__ = [
"bfs_edges",
"bfs_tree",
"bfs_predecessors",
"bfs_successors",
"descendants_at_distance",
"bfs_layers",
]

def generic_bfs_edges(G, source, neighbors=None, depth_limit=None, sort_neighbors=None):
"""Iterate over edges in a breadth-first search.

The breadth-first search begins at `source` and enqueues the
neighbors of newly visited nodes specified by the `neighbors`
function.

Parameters
----------
G : NetworkX graph

source : node
Starting node for the breadth-first search; this function
iterates over only those edges in the component reachable from
this node.

neighbors : function
A function that takes a newly visited node of the graph as input
and returns an *iterator* (not just a list) of nodes that are
neighbors of that node. If not specified, this is just the
``G.neighbors`` method, but in general it can be any function
that returns an iterator over some or all of the neighbors of a
given node, in any order.

depth_limit : int, optional(default=len(G))
Specify the maximum search depth

sort_neighbors : function
A function that takes the list of neighbors of given node as input, and
returns an *iterator* over these neighbors but with custom ordering.

Yields
------
edge
Edges in the breadth-first search starting from `source`.

Examples
--------
>>> G = nx.path_graph(3)
>>> print(list(nx.bfs_edges(G, 0)))
[(0, 1), (1, 2)]
>>> print(list(nx.bfs_edges(G, source=0, depth_limit=1)))
[(0, 1)]

Notes
-----
This implementation is from `PADS`_, which was in the public domain
when it was first accessed in July, 2004.  The modifications
to allow depth limits are based on the Wikipedia article
"`Depth-limited-search`_".

.. _Depth-limited-search: https://en.wikipedia.org/wiki/Depth-limited_search
"""
if callable(sort_neighbors):
_neighbors = neighbors
neighbors = lambda node: iter(sort_neighbors(_neighbors(node)))

visited = {source}
if depth_limit is None:
depth_limit = len(G)
queue = deque([(source, depth_limit, neighbors(source))])
while queue:
parent, depth_now, children = queue
try:
child = next(children)
if child not in visited:
yield parent, child
if depth_now > 1:
queue.append((child, depth_now - 1, neighbors(child)))
except StopIteration:
queue.popleft()

[docs]@nx._dispatch
def bfs_edges(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
"""Iterate over edges in a breadth-first-search starting at source.

Parameters
----------
G : NetworkX graph

source : node
Specify starting node for breadth-first search; this function
iterates over only those edges in the component reachable from
this node.

reverse : bool, optional
If True traverse a directed graph in the reverse direction

depth_limit : int, optional(default=len(G))
Specify the maximum search depth

sort_neighbors : function
A function that takes the list of neighbors of given node as input, and
returns an *iterator* over these neighbors but with custom ordering.

Yields
------
edge: 2-tuple of nodes
Yields edges resulting from the breadth-first search.

Examples
--------
To get the edges in a breadth-first search::

>>> G = nx.path_graph(3)
>>> list(nx.bfs_edges(G, 0))
[(0, 1), (1, 2)]
>>> list(nx.bfs_edges(G, source=0, depth_limit=1))
[(0, 1)]

To get the nodes in a breadth-first search order::

>>> G = nx.path_graph(3)
>>> root = 2
>>> edges = nx.bfs_edges(G, root)
>>> nodes = [root] + [v for u, v in edges]
>>> nodes
[2, 1, 0]

Notes
-----
The naming of this function is very similar to
:func:`~networkx.algorithms.traversal.edgebfs.edge_bfs`. The difference
is that ``edge_bfs`` yields edges even if they extend back to an already
explored node while this generator yields the edges of the tree that results
from a breadth-first-search (BFS) so no edges are reported if they extend
to already explored nodes. That means ``edge_bfs`` reports all edges while
``bfs_edges`` only reports those traversed by a node-based BFS. Yet another
description is that ``bfs_edges`` reports the edges traversed during BFS
while ``edge_bfs`` reports all edges in the order they are explored.

by D. Eppstein, July 2004; with modifications to allow depth limits
as described in _.

References
----------
..  https://en.wikipedia.org/wiki/Depth-limited_search

--------
bfs_tree
:func:`~networkx.algorithms.traversal.depth_first_search.dfs_edges`
:func:`~networkx.algorithms.traversal.edgebfs.edge_bfs`

"""
if reverse and G.is_directed():
successors = G.predecessors
else:
successors = G.neighbors
yield from generic_bfs_edges(G, source, successors, depth_limit, sort_neighbors)

[docs]def bfs_tree(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
"""Returns an oriented tree constructed from of a breadth-first-search
starting at source.

Parameters
----------
G : NetworkX graph

source : node
Specify starting node for breadth-first search

reverse : bool, optional
If True traverse a directed graph in the reverse direction

depth_limit : int, optional(default=len(G))
Specify the maximum search depth

sort_neighbors : function
A function that takes the list of neighbors of given node as input, and
returns an *iterator* over these neighbors but with custom ordering.

Returns
-------
T: NetworkX DiGraph
An oriented tree

Examples
--------
>>> G = nx.path_graph(3)
>>> print(list(nx.bfs_tree(G, 1).edges()))
[(1, 0), (1, 2)]
>>> H = nx.Graph()
>>> nx.add_path(H, [0, 1, 2, 3, 4, 5, 6])
>>> nx.add_path(H, [2, 7, 8, 9, 10])
>>> print(sorted(list(nx.bfs_tree(H, source=3, depth_limit=3).edges())))
[(1, 0), (2, 1), (2, 7), (3, 2), (3, 4), (4, 5), (5, 6), (7, 8)]

Notes
-----
by D. Eppstein, July 2004. The modifications
to allow depth limits based on the Wikipedia article
"`Depth-limited-search`_".

.. _Depth-limited-search: https://en.wikipedia.org/wiki/Depth-limited_search

--------
dfs_tree
bfs_edges
edge_bfs
"""
T = nx.DiGraph()
edges_gen = bfs_edges(
G,
source,
reverse=reverse,
depth_limit=depth_limit,
sort_neighbors=sort_neighbors,
)
return T

[docs]def bfs_predecessors(G, source, depth_limit=None, sort_neighbors=None):
"""Returns an iterator of predecessors in breadth-first-search from source.

Parameters
----------
G : NetworkX graph

source : node
Specify starting node for breadth-first search

depth_limit : int, optional(default=len(G))
Specify the maximum search depth

sort_neighbors : function
A function that takes the list of neighbors of given node as input, and
returns an *iterator* over these neighbors but with custom ordering.

Returns
-------
pred: iterator
(node, predecessor) iterator where `predecessor` is the predecessor of
`node` in a breadth first search starting from `source`.

Examples
--------
>>> G = nx.path_graph(3)
>>> print(dict(nx.bfs_predecessors(G, 0)))
{1: 0, 2: 1}
>>> H = nx.Graph()
>>> H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])
>>> print(dict(nx.bfs_predecessors(H, 0)))
{1: 0, 2: 0, 3: 1, 4: 1, 5: 2, 6: 2}
>>> M = nx.Graph()
>>> nx.add_path(M, [0, 1, 2, 3, 4, 5, 6])
>>> nx.add_path(M, [2, 7, 8, 9, 10])
>>> print(sorted(nx.bfs_predecessors(M, source=1, depth_limit=3)))
[(0, 1), (2, 1), (3, 2), (4, 3), (7, 2), (8, 7)]
>>> N = nx.DiGraph()
>>> nx.add_path(N, [0, 1, 2, 3, 4, 7])
>>> nx.add_path(N, [3, 5, 6, 7])
>>> print(sorted(nx.bfs_predecessors(N, source=2)))
[(3, 2), (4, 3), (5, 3), (6, 5), (7, 4)]

Notes
-----
by D. Eppstein, July 2004. The modifications
to allow depth limits based on the Wikipedia article
"`Depth-limited-search`_".

.. _Depth-limited-search: https://en.wikipedia.org/wiki/Depth-limited_search

--------
bfs_tree
bfs_edges
edge_bfs
"""
for s, t in bfs_edges(
G, source, depth_limit=depth_limit, sort_neighbors=sort_neighbors
):
yield (t, s)

[docs]def bfs_successors(G, source, depth_limit=None, sort_neighbors=None):
"""Returns an iterator of successors in breadth-first-search from source.

Parameters
----------
G : NetworkX graph

source : node
Specify starting node for breadth-first search

depth_limit : int, optional(default=len(G))
Specify the maximum search depth

sort_neighbors : function
A function that takes the list of neighbors of given node as input, and
returns an *iterator* over these neighbors but with custom ordering.

Returns
-------
succ: iterator
(node, successors) iterator where `successors` is the non-empty list of
successors of `node` in a breadth first search from `source`.
To appear in the iterator, `node` must have successors.

Examples
--------
>>> G = nx.path_graph(3)
>>> print(dict(nx.bfs_successors(G, 0)))
{0: , 1: }
>>> H = nx.Graph()
>>> H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])
>>> print(dict(nx.bfs_successors(H, 0)))
{0: [1, 2], 1: [3, 4], 2: [5, 6]}
>>> G = nx.Graph()
>>> nx.add_path(G, [0, 1, 2, 3, 4, 5, 6])
>>> nx.add_path(G, [2, 7, 8, 9, 10])
>>> print(dict(nx.bfs_successors(G, source=1, depth_limit=3)))
{1: [0, 2], 2: [3, 7], 3: , 7: }
>>> G = nx.DiGraph()
>>> nx.add_path(G, [0, 1, 2, 3, 4, 5])
>>> print(dict(nx.bfs_successors(G, source=3)))
{3: , 4: }

Notes
-----
by D. Eppstein, July 2004.The modifications
to allow depth limits based on the Wikipedia article
"`Depth-limited-search`_".

.. _Depth-limited-search: https://en.wikipedia.org/wiki/Depth-limited_search

--------
bfs_tree
bfs_edges
edge_bfs
"""
parent = source
children = []
for p, c in bfs_edges(
G, source, depth_limit=depth_limit, sort_neighbors=sort_neighbors
):
if p == parent:
children.append(c)
continue
yield (parent, children)
children = [c]
parent = p
yield (parent, children)

[docs]def bfs_layers(G, sources):
"""Returns an iterator of all the layers in breadth-first search traversal.

Parameters
----------
G : NetworkX graph
A graph over which to find the layers using breadth-first search.

sources : node in `G` or list of nodes in `G`
Specify starting nodes for single source or multiple sources breadth-first search

Yields
------
layer: list of nodes
Yields list of nodes at the same distance from sources

Examples
--------
>>> G = nx.path_graph(5)
>>> dict(enumerate(nx.bfs_layers(G, [0, 4])))
{0: [0, 4], 1: [1, 3], 2: }
>>> H = nx.Graph()
>>> H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])
>>> dict(enumerate(nx.bfs_layers(H, )))
{0: , 1: [0, 3, 4], 2: , 3: [5, 6]}
>>> dict(enumerate(nx.bfs_layers(H, [1, 6])))
{0: [1, 6], 1: [0, 3, 4, 2], 2: }
"""
if sources in G:
sources = [sources]

current_layer = list(sources)
visited = set(sources)

for source in current_layer:
if source not in G:
raise nx.NetworkXError(f"The node {source} is not in the graph.")

# this is basically BFS, except that the current layer only stores the nodes at
# same distance from sources at each iteration
while current_layer:
yield current_layer
next_layer = list()
for node in current_layer:
for child in G[node]:
if child not in visited:
next_layer.append(child)
current_layer = next_layer

[docs]def descendants_at_distance(G, source, distance):
"""Returns all nodes at a fixed `distance` from `source` in `G`.

Parameters
----------
G : NetworkX graph
A graph
source : node in `G`
distance : the distance of the wanted nodes from `source`

Returns
-------
set()
The descendants of `source` in `G` at the given `distance` from `source`

Examples
--------
>>> G = nx.path_graph(5)
>>> nx.descendants_at_distance(G, 2, 2)
{0, 4}
>>> H = nx.DiGraph()
>>> H.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6)])
>>> nx.descendants_at_distance(H, 0, 2)
{3, 4, 5, 6}
>>> nx.descendants_at_distance(H, 5, 0)
{5}
>>> nx.descendants_at_distance(H, 5, 1)
set()
"""
if source not in G:
raise nx.NetworkXError(f"The node {source} is not in the graph.")

bfs_generator = nx.bfs_layers(G, source)
for i, layer in enumerate(bfs_generator):
if i == distance:
return set(layer)
return set()
```