Fast compiled graphs
This is a Cython implementation of the base class for sparse and dense graphs in Sage. It is not intended for use on its own. Specific graph types should extend this base class and implement missing functionalities. Whenever possible, specific methods should also be overridden with implementations that suit the graph type under consideration.
The class CGraph maintains the following variables:
The bitset active_vertices is a list of all available vertices for use, but only the ones which are set are considered to actually be in the graph. The variables num_verts and num_arcs are self-explanatory. Note that num_verts is the number of bits set in active_vertices, not the full length of the bitset. The arrays in_degrees and out_degrees are of the same length as the bitset.
For more information about active vertices, see the documentation for the method realloc.
Bases: object
Compiled sparse and dense graphs.
Add the given arc to this graph.
INPUT:
OUTPUT:
EXAMPLE:
sage: from sage.graphs.base.c_graph import CGraph
sage: G = CGraph()
sage: G.add_arc(0, 1)
Traceback (most recent call last):
...
NotImplementedError
Adds vertex k to the graph.
INPUT:
OUTPUT:
See also
EXAMPLES:
Adding vertices to a sparse graph:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: G = SparseGraph(3, extra_vertices=3)
sage: G.add_vertex(3)
3
sage: G.add_arc(2, 5)
Traceback (most recent call last):
...
LookupError: Vertex (5) is not a vertex of the graph.
sage: G.add_arc(1, 3)
sage: G.has_arc(1, 3)
True
sage: G.has_arc(2, 3)
False
Adding vertices to a dense graph:
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: G = DenseGraph(3, extra_vertices=3)
sage: G.add_vertex(3)
3
sage: G.add_arc(2,5)
Traceback (most recent call last):
...
LookupError: Vertex (5) is not a vertex of the graph.
sage: G.add_arc(1, 3)
sage: G.has_arc(1, 3)
True
sage: G.has_arc(2, 3)
False
Repeatedly adding a vertex using \(k = -1\) will allocate more memory as required:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: G = SparseGraph(3, extra_vertices=0)
sage: G.verts()
[0, 1, 2]
sage: for i in range(10):
... _ = G.add_vertex(-1);
...
sage: G.verts()
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: G = DenseGraph(3, extra_vertices=0)
sage: G.verts()
[0, 1, 2]
sage: for i in range(12):
... _ = G.add_vertex(-1);
...
sage: G.verts()
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
TESTS:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: G = SparseGraph(3, extra_vertices=0)
sage: G.add_vertex(6)
Traceback (most recent call last):
...
RuntimeError: Requested vertex is past twice the allocated range: use realloc.
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: G = DenseGraph(3, extra_vertices=0)
sage: G.add_vertex(6)
Traceback (most recent call last):
...
RuntimeError: Requested vertex is past twice the allocated range: use realloc.
Adds vertices from the iterable verts.
INPUT:
OUTPUT:
List of generated labels if there is any -1 in verts. None otherwise.
See also
EXAMPLE:
Adding vertices for sparse graphs:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: S = SparseGraph(nverts=4, extra_vertices=4)
sage: S.verts()
[0, 1, 2, 3]
sage: S.add_vertices([3,-1,4,9])
[5]
sage: S.verts()
[0, 1, 2, 3, 4, 5, 9]
sage: S.realloc(20)
sage: S.verts()
[0, 1, 2, 3, 4, 5, 9]
Adding vertices for dense graphs:
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: D = DenseGraph(nverts=4, extra_vertices=4)
sage: D.verts()
[0, 1, 2, 3]
sage: D.add_vertices([3,-1,4,9])
[5]
sage: D.verts()
[0, 1, 2, 3, 4, 5, 9]
sage: D.realloc(20)
sage: D.verts()
[0, 1, 2, 3, 4, 5, 9]
Return the labels of all arcs from u to v.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: from sage.graphs.base.c_graph import CGraph
sage: G = CGraph()
sage: G.all_arcs(0, 1)
Traceback (most recent call last):
...
NotImplementedError
Checks that n is a vertex of self.
This method is different from has_vertex(). The current method raises an error if n is not a vertex of this graph. On the other hand, has_vertex() returns a boolean to signify whether or not n is a vertex of this graph.
INPUT:
OUTPUT:
See also
EXAMPLES:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: S = SparseGraph(nverts=10, expected_degree=3, extra_vertices=10)
sage: S.check_vertex(4)
sage: S.check_vertex(12)
Traceback (most recent call last):
...
LookupError: Vertex (12) is not a vertex of the graph.
sage: S.check_vertex(24)
Traceback (most recent call last):
...
LookupError: Vertex (24) is not a vertex of the graph.
sage: S.check_vertex(-19)
Traceback (most recent call last):
...
LookupError: Vertex (-19) is not a vertex of the graph.
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: D = DenseGraph(nverts=10, extra_vertices=10)
sage: D.check_vertex(4)
sage: D.check_vertex(12)
Traceback (most recent call last):
...
LookupError: Vertex (12) is not a vertex of the graph.
sage: D.check_vertex(24)
Traceback (most recent call last):
...
LookupError: Vertex (24) is not a vertex of the graph.
sage: D.check_vertex(-19)
Traceback (most recent call last):
...
LookupError: Vertex (-19) is not a vertex of the graph.
Report the number of vertices allocated.
INPUT:
OUTPUT:
EXAMPLES:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: S = SparseGraph(nverts=4, extra_vertices=4)
sage: S.current_allocation()
8
sage: S.add_vertex(6)
6
sage: S.current_allocation()
8
sage: S.add_vertex(10)
10
sage: S.current_allocation()
16
sage: S.add_vertex(40)
Traceback (most recent call last):
...
RuntimeError: Requested vertex is past twice the allocated range: use realloc.
sage: S.realloc(50)
sage: S.add_vertex(40)
40
sage: S.current_allocation()
50
sage: S.realloc(30)
-1
sage: S.current_allocation()
50
sage: S.del_vertex(40)
sage: S.realloc(30)
sage: S.current_allocation()
30
The actual number of vertices in a graph might be less than the number of vertices allocated for the graph:
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: G = DenseGraph(nverts=3, extra_vertices=2)
sage: order = len(G.verts())
sage: order
3
sage: G.current_allocation()
5
sage: order < G.current_allocation()
True
Delete all arcs from u to v.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: from sage.graphs.base.c_graph import CGraph
sage: G = CGraph()
sage: G.del_all_arcs(0,1)
Traceback (most recent call last):
...
NotImplementedError
Deletes the vertex v, along with all edges incident to it. If v is not in self, fails silently.
INPUT:
OUTPUT:
See also
EXAMPLES:
Deleting vertices of sparse graphs:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: G = SparseGraph(3)
sage: G.add_arc(0, 1)
sage: G.add_arc(0, 2)
sage: G.add_arc(1, 2)
sage: G.add_arc(2, 0)
sage: G.del_vertex(2)
sage: for i in range(2):
... for j in range(2):
... if G.has_arc(i, j):
... print i, j
0 1
sage: G = SparseGraph(3)
sage: G.add_arc(0, 1)
sage: G.add_arc(0, 2)
sage: G.add_arc(1, 2)
sage: G.add_arc(2, 0)
sage: G.del_vertex(1)
sage: for i in xrange(3):
... for j in xrange(3):
... if G.has_arc(i, j):
... print i, j
0 2
2 0
Deleting vertices of dense graphs:
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: G = DenseGraph(4)
sage: G.add_arc(0, 1); G.add_arc(0, 2)
sage: G.add_arc(3, 1); G.add_arc(3, 2)
sage: G.add_arc(1, 2)
sage: G.verts()
[0, 1, 2, 3]
sage: G.del_vertex(3); G.verts()
[0, 1, 2]
sage: for i in range(3):
... for j in range(3):
... if G.has_arc(i, j):
... print i, j
...
0 1
0 2
1 2
If the vertex to be deleted is not in this graph, then fail silently:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: G = SparseGraph(3)
sage: G.verts()
[0, 1, 2]
sage: G.has_vertex(3)
False
sage: G.del_vertex(3)
sage: G.verts()
[0, 1, 2]
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: G = DenseGraph(5)
sage: G.verts()
[0, 1, 2, 3, 4]
sage: G.has_vertex(6)
False
sage: G.del_vertex(6)
sage: G.verts()
[0, 1, 2, 3, 4]
Determine whether or not the given arc is in this graph.
INPUT:
OUTPUT:
EXAMPLE:
sage: from sage.graphs.base.c_graph import CGraph
sage: G = CGraph()
sage: G.has_arc(0, 1)
Not Implemented!
False
Determine whether the vertex n is in self.
This method is different from check_vertex(). The current method returns a boolean to signify whether or not n is a vertex of this graph. On the other hand, check_vertex() raises an error if n is not a vertex of this graph.
INPUT:
OUTPUT:
See also
EXAMPLES:
Upon initialization, a SparseGraph or DenseGraph has the first nverts vertices:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: S = SparseGraph(nverts=10, expected_degree=3, extra_vertices=10)
sage: S.has_vertex(6)
True
sage: S.has_vertex(12)
False
sage: S.has_vertex(24)
False
sage: S.has_vertex(-19)
False
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: D = DenseGraph(nverts=10, extra_vertices=10)
sage: D.has_vertex(6)
True
sage: D.has_vertex(12)
False
sage: D.has_vertex(24)
False
sage: D.has_vertex(-19)
False
Gives the in-neighbors of the vertex v.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: from sage.graphs.base.c_graph import CGraph
sage: G = CGraph()
sage: G.in_neighbors(0)
Traceback (most recent call last):
...
NotImplementedError
Gives the out-neighbors of the vertex u.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: from sage.graphs.base.c_graph import CGraph
sage: G = CGraph()
sage: G.out_neighbors(0)
Traceback (most recent call last):
...
NotImplementedError
Reallocate the number of vertices to use, without actually adding any.
INPUT:
OUTPUT:
See also
EXAMPLES:
First, note that realloc() is implemented for SparseGraph and DenseGraph differently, and is not implemented at the CGraph level:
sage: from sage.graphs.base.c_graph import CGraph
sage: G = CGraph()
sage: G.realloc(20)
Traceback (most recent call last):
...
NotImplementedError
The realloc implementation for sparse graphs:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: S = SparseGraph(nverts=4, extra_vertices=4)
sage: S.current_allocation()
8
sage: S.add_vertex(6)
6
sage: S.current_allocation()
8
sage: S.add_vertex(10)
10
sage: S.current_allocation()
16
sage: S.add_vertex(40)
Traceback (most recent call last):
...
RuntimeError: Requested vertex is past twice the allocated range: use realloc.
sage: S.realloc(50)
sage: S.add_vertex(40)
40
sage: S.current_allocation()
50
sage: S.realloc(30)
-1
sage: S.current_allocation()
50
sage: S.del_vertex(40)
sage: S.realloc(30)
sage: S.current_allocation()
30
The realloc implementation for dense graphs:
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: D = DenseGraph(nverts=4, extra_vertices=4)
sage: D.current_allocation()
8
sage: D.add_vertex(6)
6
sage: D.current_allocation()
8
sage: D.add_vertex(10)
10
sage: D.current_allocation()
16
sage: D.add_vertex(40)
Traceback (most recent call last):
...
RuntimeError: Requested vertex is past twice the allocated range: use realloc.
sage: D.realloc(50)
sage: D.add_vertex(40)
40
sage: D.current_allocation()
50
sage: D.realloc(30)
-1
sage: D.current_allocation()
50
sage: D.del_vertex(40)
sage: D.realloc(30)
sage: D.current_allocation()
30
Returns a list of the vertices in self.
INPUT:
OUTPUT:
EXAMPLE:
sage: from sage.graphs.base.sparse_graph import SparseGraph
sage: S = SparseGraph(nverts=4, extra_vertices=4)
sage: S.verts()
[0, 1, 2, 3]
sage: S.add_vertices([3,5,7,9])
sage: S.verts()
[0, 1, 2, 3, 5, 7, 9]
sage: S.realloc(20)
sage: S.verts()
[0, 1, 2, 3, 5, 7, 9]
sage: from sage.graphs.base.dense_graph import DenseGraph
sage: G = DenseGraph(3, extra_vertices=2)
sage: G.verts()
[0, 1, 2]
sage: G.del_vertex(0)
sage: G.verts()
[1, 2]
Bases: sage.graphs.base.graph_backends.GenericGraphBackend
Base class for sparse and dense graph backends.
sage: from sage.graphs.base.c_graph import CGraphBackend
This class is extended by SparseGraphBackend and DenseGraphBackend, which are fully functional backends. This class is mainly just for vertex functions, which are the same for both. A CGraphBackend will not work on its own:
sage: from sage.graphs.base.c_graph import CGraphBackend
sage: CGB = CGraphBackend()
sage: CGB.degree(0, True)
Traceback (most recent call last):
...
AttributeError: 'CGraphBackend' object has no attribute 'vertex_ints'
The appropriate way to use these backends is via Sage graphs:
sage: G = Graph(30, implementation="c_graph")
sage: G.add_edges([(0,1), (0,3), (4,5), (9, 23)])
sage: G.edges(labels=False)
[(0, 1), (0, 3), (4, 5), (9, 23)]
See also
Add a vertex to self.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: D = sage.graphs.base.dense_graph.DenseGraphBackend(9)
sage: D.add_vertex(10)
sage: D.add_vertex([])
Traceback (most recent call last):
...
TypeError: unhashable type: 'list'
sage: S = sage.graphs.base.sparse_graph.SparseGraphBackend(9)
sage: S.add_vertex(10)
sage: S.add_vertex([])
Traceback (most recent call last):
...
TypeError: unhashable type: 'list'
Add vertices to self.
INPUT:
OUTPUT:
Generated names of new vertices if there is at least one None value present in vertices. None otherwise.
See also
EXAMPLE:
sage: D = sage.graphs.base.sparse_graph.SparseGraphBackend(1)
sage: D.add_vertices([1,2,3])
sage: D.add_vertices([None]*4)
[4, 5, 6, 7]
sage: G = sage.graphs.base.sparse_graph.SparseGraphBackend(0)
sage: G.add_vertices([0,1])
sage: list(G.iterator_verts(None))
[0, 1]
sage: list(G.iterator_edges([0,1], True))
[]
sage: import sage.graphs.base.dense_graph
sage: D = sage.graphs.base.dense_graph.DenseGraphBackend(9)
sage: D.add_vertices([10,11,12])
Returns the shortest path between x and y using a bidirectional version of Dijkstra’s algorithm.
INPUT:
OUTPUT:
EXAMPLE:
sage: G = Graph(graphs.PetersenGraph(), implementation="c_graph")
sage: for (u,v) in G.edges(labels=None):
... G.set_edge_label(u,v,1)
sage: G.shortest_path(0, 1, by_weight=True)
[0, 1]
TEST:
Bugfix from #7673
sage: G = Graph(implementation="networkx")
sage: G.add_edges([(0,1,9),(0,2,8),(1,2,7)])
sage: Gc = G.copy(implementation='c_graph')
sage: sp = G.shortest_path_length(0,1,by_weight=True)
sage: spc = Gc.shortest_path_length(0,1,by_weight=True)
sage: sp == spc
True
Returns a breadth-first search from vertex v.
INPUT:
ALGORITHM:
Below is a general template for breadth-first search.
See also
EXAMPLES:
Breadth-first search of the Petersen graph starting at vertex 0:
sage: G = Graph(graphs.PetersenGraph(), implementation="c_graph")
sage: list(G.breadth_first_search(0))
[0, 1, 4, 5, 2, 6, 3, 9, 7, 8]
Visiting German cities using breadth-first search:
sage: G = Graph({"Mannheim": ["Frankfurt","Karlsruhe"],
... "Frankfurt": ["Mannheim","Wurzburg","Kassel"],
... "Kassel": ["Frankfurt","Munchen"],
... "Munchen": ["Kassel","Nurnberg","Augsburg"],
... "Augsburg": ["Munchen","Karlsruhe"],
... "Karlsruhe": ["Mannheim","Augsburg"],
... "Wurzburg": ["Frankfurt","Erfurt","Nurnberg"],
... "Nurnberg": ["Wurzburg","Stuttgart","Munchen"],
... "Stuttgart": ["Nurnberg"],
... "Erfurt": ["Wurzburg"]}, implementation="c_graph")
sage: list(G.breadth_first_search("Frankfurt"))
['Frankfurt', 'Mannheim', 'Kassel', 'Wurzburg', 'Karlsruhe', 'Munchen', 'Erfurt', 'Nurnberg', 'Augsburg', 'Stuttgart']
Return the degree of the vertex v.
INPUT:
OUTPUT:
EXAMPLE:
sage: from sage.graphs.base.sparse_graph import SparseGraphBackend
sage: B = SparseGraphBackend(7)
sage: B.degree(3, False)
0
TESTS:
Ensure that ticket trac ticket #8395 is fixed.
sage: def my_add_edges(G, m, n):
... for i in range(m):
... u = randint(0, n)
... v = randint(0, n)
... G.add_edge(u, v)
sage: G = Graph({1:[1]}); G
Looped graph on 1 vertex
sage: G.edges(labels=False)
[(1, 1)]
sage: G.degree(); G.size()
[2]
1
sage: sum(G.degree()) == 2 * G.size()
True
sage: G = Graph({1:[1,2], 2:[2,3]}, loops=True); G
Looped graph on 3 vertices
sage: my_add_edges(G, 100, 50)
sage: sum(G.degree()) == 2 * G.size()
True
sage: G = Graph({1:[2,2], 2:[3]}); G
Multi-graph on 3 vertices
sage: G.edges(labels=False)
[(1, 2), (1, 2), (2, 3)]
sage: G.degree(); G.size()
[2, 3, 1]
3
sage: sum(G.degree()) == 2 * G.size()
True
sage: G.allow_loops(True); G
Looped multi-graph on 3 vertices
sage: my_add_edges(G, 100, 50)
sage: sum(G.degree()) == 2 * G.size()
True
sage: D = DiGraph({1:[2], 2:[1,3]}); D
Digraph on 3 vertices
sage: D.edges(labels=False)
[(1, 2), (2, 1), (2, 3)]
sage: D.degree(); D.size()
[2, 3, 1]
3
sage: sum(D.degree()) == 2 * D.size()
True
sage: D.allow_loops(True); D
Looped digraph on 3 vertices
sage: my_add_edges(D, 100, 50)
sage: sum(D.degree()) == 2 * D.size()
True
sage: D.allow_multiple_edges(True)
sage: my_add_edges(D, 200, 50)
sage: sum(D.degree()) == 2 * D.size()
True
sage: G = Graph({1:[2,2,2]})
sage: G.allow_loops(True)
sage: G.add_edge(1,1)
sage: G.add_edge(1,1)
sage: G.edges(labels=False)
[(1, 1), (1, 1), (1, 2), (1, 2), (1, 2)]
sage: G.degree(1)
7
sage: G.allow_loops(False)
sage: G.edges(labels=False)
[(1, 2), (1, 2), (1, 2)]
sage: G.degree(1)
3
sage: G = Graph({1:{2:['a','a','a']}})
sage: G.allow_loops(True)
sage: G.add_edge(1,1,'b')
sage: G.add_edge(1,1,'b')
sage: G.add_edge(1,1)
sage: G.add_edge(1,1)
sage: G.edges()
[(1, 1, None), (1, 1, None), (1, 1, 'b'), (1, 1, 'b'), (1, 2, 'a'), (1, 2, 'a'), (1, 2, 'a')]
sage: G.degree(1)
11
sage: G.allow_loops(False)
sage: G.edges()
[(1, 2, 'a'), (1, 2, 'a'), (1, 2, 'a')]
sage: G.degree(1)
3
sage: G = Graph({1:{2:['a','a','a']}})
sage: G.allow_loops(True)
sage: G.add_edge(1,1,'b')
sage: G.add_edge(1,1,'b')
sage: G.edges()
[(1, 1, 'b'), (1, 1, 'b'), (1, 2, 'a'), (1, 2, 'a'), (1, 2, 'a')]
sage: G.degree(1)
7
sage: G.allow_loops(False)
sage: G.edges()
[(1, 2, 'a'), (1, 2, 'a'), (1, 2, 'a')]
sage: G.degree(1)
3
Ensure that trac ticket #13664 is fixed
sage: W = WeylGroup(["A",1])
sage: G = W.cayley_graph()
sage: Graph(G).degree()
[1, 1]
sage: h = Graph()
sage: h.add_edge(1,2,"a")
sage: h.add_edge(1,2,"a")
sage: h.degree()
[1, 1]
Delete a vertex in self, failing silently if the vertex is not in the graph.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: D = sage.graphs.base.dense_graph.DenseGraphBackend(9)
sage: D.del_vertex(0)
sage: D.has_vertex(0)
False
sage: S = sage.graphs.base.sparse_graph.SparseGraphBackend(9)
sage: S.del_vertex(0)
sage: S.has_vertex(0)
False
Delete vertices from an iterable container.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: import sage.graphs.base.dense_graph
sage: D = sage.graphs.base.dense_graph.DenseGraphBackend(9)
sage: D.del_vertices([7,8])
sage: D.has_vertex(7)
False
sage: D.has_vertex(6)
True
sage: D = sage.graphs.base.sparse_graph.SparseGraphBackend(9)
sage: D.del_vertices([1,2,3])
sage: D.has_vertex(1)
False
sage: D.has_vertex(0)
True
Returns a depth-first search from vertex v.
INPUT:
ALGORITHM:
Below is a general template for depth-first search.
See also
EXAMPLES:
Traversing the Petersen graph using depth-first search:
sage: G = Graph(graphs.PetersenGraph(), implementation="c_graph")
sage: list(G.depth_first_search(0))
[0, 5, 8, 6, 9, 7, 2, 3, 4, 1]
Visiting German cities using depth-first search:
sage: G = Graph({"Mannheim": ["Frankfurt","Karlsruhe"],
... "Frankfurt": ["Mannheim","Wurzburg","Kassel"],
... "Kassel": ["Frankfurt","Munchen"],
... "Munchen": ["Kassel","Nurnberg","Augsburg"],
... "Augsburg": ["Munchen","Karlsruhe"],
... "Karlsruhe": ["Mannheim","Augsburg"],
... "Wurzburg": ["Frankfurt","Erfurt","Nurnberg"],
... "Nurnberg": ["Wurzburg","Stuttgart","Munchen"],
... "Stuttgart": ["Nurnberg"],
... "Erfurt": ["Wurzburg"]}, implementation="c_graph")
sage: list(G.depth_first_search("Frankfurt"))
['Frankfurt', 'Wurzburg', 'Nurnberg', 'Munchen', 'Kassel', 'Augsburg', 'Karlsruhe', 'Mannheim', 'Stuttgart', 'Erfurt']
Returns whether v is a vertex of self.
INPUT:
OUTPUT:
EXAMPLE:
sage: from sage.graphs.base.sparse_graph import SparseGraphBackend
sage: B = SparseGraphBackend(7)
sage: B.has_vertex(6)
True
sage: B.has_vertex(7)
False
Returns whether the graph is connected.
EXAMPLES:
Petersen’s graph is connected:
sage: DiGraph(graphs.PetersenGraph(),implementation="c_graph").is_connected()
True
While the disjoint union of two of them is not:
sage: DiGraph(2*graphs.PetersenGraph(),implementation="c_graph").is_connected()
False
A graph with non-integer vertex labels:
sage: Graph(graphs.CubeGraph(3), implementation='c_graph').is_connected()
True
Returns whether the graph is both directed and acylic (possibly with a certificate)
INPUT:
OUTPUT:
When certificate=False, returns a boolean value. When certificate=True :
- If the graph is acyclic, returns a pair (True, ordering) where ordering is a list of the vertices such that u appears before v in ordering if u, v is an edge.
- Else, returns a pair (False, cycle) where cycle is a list of vertices representing a circuit in the graph.
ALGORITHM:
We pick a vertex at random, think hard and find out that that if we are to remove the vertex from the graph we must remove all of its out-neighbors in the first place. So we put all of its out-neighbours in a stack, and repeat the same procedure with the vertex on top of the stack (when a vertex on top of the stack has no out-neighbors, we remove it immediately). Of course, for each vertex we only add its outneighbors to the end of the stack once : if for some reason the previous algorithm leads us to do it twice, it means we have found a circuit.
We keep track of the vertices whose out-neighborhood has been added to the stack once with a variable named tried.
There is no reason why the graph should be empty at the end of this procedure, so we run it again on the remaining vertices until none are left or a circuit is found.
Note
The graph is assumed to be directed. An exception is raised if it is not.
EXAMPLES:
At first, the following graph is acyclic:
sage: D = DiGraph({ 0:[1,2,3], 4:[2,5], 1:[8], 2:[7], 3:[7], 5:[6,7], 7:[8], 6:[9], 8:[10], 9:[10] })
sage: D.plot(layout='circular').show()
sage: D.is_directed_acyclic()
True
Adding an edge from \(9\) to \(7\) does not change it:
sage: D.add_edge(9,7)
sage: D.is_directed_acyclic()
True
We can obtain as a proof an ordering of the vertices such that \(u\) appears before \(v\) if \(uv\) is an edge of the graph:
sage: D.is_directed_acyclic(certificate = True)
(True, [4, 5, 6, 9, 0, 1, 2, 3, 7, 8, 10])
Adding an edge from 7 to 4, though, makes a difference:
sage: D.add_edge(7,4)
sage: D.is_directed_acyclic()
False
Indeed, it creates a circuit \(7, 4, 5\):
sage: D.is_directed_acyclic(certificate = True)
(False, [7, 4, 5])
Checking acyclic graphs are indeed acyclic
sage: def random_acyclic(n, p):
... g = graphs.RandomGNP(n, p)
... h = DiGraph()
... h.add_edges([ ((u,v) if u<v else (v,u)) for u,v,_ in g.edges() ])
... return h
...
sage: all( random_acyclic(100, .2).is_directed_acyclic() # long time
... for i in range(50)) # long time
True
Returns whether the graph is strongly connected.
EXAMPLES:
The circuit on 3 vertices is obviously strongly connected:
sage: g = DiGraph({0: [1], 1: [2], 2: [0]}, implementation="c_graph")
sage: g.is_strongly_connected()
True
But a transitive triangle is not:
sage: g = DiGraph({0: [1,2], 1: [2]}, implementation="c_graph")
sage: g.is_strongly_connected()
False
Returns an iterator over the incoming neighbors of v.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: P = DiGraph(graphs.PetersenGraph().to_directed(), implementation="c_graph")
sage: list(P._backend.iterator_in_nbrs(0))
[1, 4, 5]
Returns an iterator over the neighbors of v.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: P = Graph(graphs.PetersenGraph(), implementation="c_graph")
sage: list(P._backend.iterator_nbrs(0))
[1, 4, 5]
Returns an iterator over the outgoing neighbors of v.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: P = DiGraph(graphs.PetersenGraph().to_directed(), implementation="c_graph")
sage: list(P._backend.iterator_out_nbrs(0))
[1, 4, 5]
Returns an iterator over the vertices of self intersected with verts.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: P = Graph(graphs.PetersenGraph(), implementation="c_graph")
sage: list(P._backend.iterator_verts(P))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
sage: list(P._backend.iterator_verts())
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
sage: list(P._backend.iterator_verts([1, 2, 3]))
[1, 2, 3]
sage: list(P._backend.iterator_verts([1, 2, 10]))
[1, 2]
Returns whether loops are allowed in this graph.
INPUT:
OUTPUT:
EXAMPLE:
sage: G = Graph(implementation='c_graph')
sage: G._backend.loops()
False
sage: G._backend.loops(True)
sage: G._backend.loops()
True
Returns the number of edges in self.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: G = Graph(graphs.PetersenGraph(), implementation="c_graph")
sage: G._backend.num_edges(False)
15
TESTS:
Ensure that ticket #8395 is fixed.
sage: G = Graph({1:[1]}); G
Looped graph on 1 vertex
sage: G.edges(labels=False)
[(1, 1)]
sage: G.size()
1
sage: G = Graph({1:[2,2]}); G
Multi-graph on 2 vertices
sage: G.edges(labels=False)
[(1, 2), (1, 2)]
sage: G.size()
2
sage: G = Graph({1:[1,1]}); G
Looped multi-graph on 1 vertex
sage: G.edges(labels=False)
[(1, 1), (1, 1)]
sage: G.size()
2
sage: D = DiGraph({1:[1]}); D
Looped digraph on 1 vertex
sage: D.edges(labels=False)
[(1, 1)]
sage: D.size()
1
sage: D = DiGraph({1:[2,2], 2:[1,1]}); D
Multi-digraph on 2 vertices
sage: D.edges(labels=False)
[(1, 2), (1, 2), (2, 1), (2, 1)]
sage: D.size()
4
sage: D = DiGraph({1:[1,1]}); D
Looped multi-digraph on 1 vertex
sage: D.edges(labels=False)
[(1, 1), (1, 1)]
sage: D.size()
2
sage: from sage.graphs.base.sparse_graph import SparseGraphBackend
sage: S = SparseGraphBackend(7)
sage: S.num_edges(directed=False)
0
sage: S.loops(True)
sage: S.add_edge(1, 1, None, directed=False)
sage: S.num_edges(directed=False)
1
sage: S.multiple_edges(True)
sage: S.add_edge(1, 1, None, directed=False)
sage: S.num_edges(directed=False)
2
sage: from sage.graphs.base.dense_graph import DenseGraphBackend
sage: D = DenseGraphBackend(7)
sage: D.num_edges(directed=False)
0
sage: D.loops(True)
sage: D.add_edge(1, 1, None, directed=False)
sage: D.num_edges(directed=False)
1
Returns the number of vertices in self.
INPUT:
OUTPUT:
See also
EXAMPLE:
sage: G = Graph(graphs.PetersenGraph(), implementation="c_graph")
sage: G._backend.num_verts()
10
Returns the out-degree of v
INPUT:
EXAMPLE:
sage: D = DiGraph( { 0: [1,2,3], 1: [0,2], 2: [3], 3: [4], 4: [0,5], 5: [1] } )
sage: D.out_degree(1)
2
Relabels the graph according to perm.
INPUT:
EXAMPLES:
sage: G = Graph(graphs.PetersenGraph(), implementation="c_graph")
sage: G._backend.relabel(range(9,-1,-1), False)
sage: G.edges()
[(0, 2, None),
(0, 3, None),
(0, 5, None),
(1, 3, None),
(1, 4, None),
(1, 6, None),
(2, 4, None),
(2, 7, None),
(3, 8, None),
(4, 9, None),
(5, 6, None),
(5, 9, None),
(6, 7, None),
(7, 8, None),
(8, 9, None)]
Returns the shortest path between x and y.
INPUT:
OUTPUT:
EXAMPLE:
sage: G = Graph(graphs.PetersenGraph(), implementation="c_graph")
sage: G.shortest_path(0, 1)
[0, 1]
Returns for each vertex u a shortest v-u path.
INPUT:
OUTPUT:
Note
The weight of edges is not taken into account.
ALGORITHM:
This is just a breadth-first search.
EXAMPLES:
On the Petersen Graph:
sage: g = graphs.PetersenGraph()
sage: paths = g._backend.shortest_path_all_vertices(0)
sage: all([ len(paths[v]) == 0 or len(paths[v])-1 == g.distance(0,v) for v in g])
True
On a disconnected graph
sage: g = 2*graphs.RandomGNP(20,.3)
sage: paths = g._backend.shortest_path_all_vertices(0)
sage: all([ (v not in paths and g.distance(0,v) == +Infinity) or len(paths[v])-1 == g.distance(0,v) for v in g])
True
Returns the strongly connected component containing the given vertex.
INPUT:
EXAMPLES:
The digraph obtained from the PetersenGraph has an unique strongly connected component:
sage: g = DiGraph(graphs.PetersenGraph())
sage: g.strongly_connected_component_containing_vertex(0)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
In the Butterfly DiGraph, each vertex is a strongly connected component:
sage: g = digraphs.ButterflyGraph(3)
sage: all([[v] == g.strongly_connected_component_containing_vertex(v) for v in g])
True
Bases: object
An iterator for traversing a (di)graph.
This class is commonly used to perform a depth-first or breadth-first search. The class does not build all at once in memory the whole list of visited vertices. The class maintains the following variables:
EXAMPLE:
sage: g = graphs.PetersenGraph()
sage: list(g.breadth_first_search(0))
[0, 1, 4, 5, 2, 6, 3, 9, 7, 8]
x.next() -> the next value, or raise StopIteration