Template Class DiGraphS

Page Contents

Inheritance Relationships

Base Type

Template Parameter Order

  1. typename nodeview_t

  2. typename adjlist_t

Class Documentation

template<typename nodeview_t, typename adjlist_t = py::dict<Value_type<nodeview_t>, int>>
class DiGraphS : public xn::Graph<nodeview_t, adjlist_t>

Base class for directed graphs.

A DiGraphS stores nodes and edges with optional data, or attributes.

DiGraphSs hold directed edges. Self loops are allowed but multiple (parallel) edges are not.

Nodes can be arbitrary (hashable) C++ objects with optional key/value attributes. By convention None is not used as a node.

Edges are represented as links between nodes with optional key/value attributes.

Parameters

node_container : input graph (optional, default: None) Data to initialize graph. If None (default) an empty graph is created. The data can be any format that is supported by the to_networkx_graph() function, currently including edge list, dict of dicts, dict of lists, NetworkX graph, NumPy matrix or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph.

See Also

Graph DiGraph MultiGraph MultiDiGraph OrderedGraph

Examples

Create an empty graph structure (a “null graph”) with 5 nodes and no edges.

auto v = std::vector{3, 4, 2, 8}; auto G = xn::DiGraphS(v);

auto va = py::dict{{3, 0.1}, {4, 0.5}, {2, 0.2}}; auto G = xn::DiGraphS(va);

auto r = py::range(100); auto G = xn::DiGraphS(r);

G can be grown in several ways.

Nodes:**

Add one node at a time:

G.add_node(1)

Add the nodes from any container (a list, dict, set or even the lines from a file or the nodes from another graph).

G.add_nodes_from([2, 3]) G.add_nodes_from(range(100, 110)) H = xn::path_graph(10) G.add_nodes_from(H)

In addition to strings and integers any hashable C++ object (except None) can represent a node, e.g. a customized node object, or even another DiGraphS.

G.add_node(H)

Edges:**

G can also be grown by adding edges.

Add one edge,

G.add_edge(1, 2);

a list of edges,

G.add_edges_from([(1, 2), (1, 3)]);

or a collection of edges,

G.add_edges_from(H.edges());

If some edges connect nodes not yet in the graph, the nodes are added automatically. There are no errors when adding nodes or edges that already exist.

Attributes:**

Each graph can hold key/value attribute pairs in an associated attribute dictionary (the keys must be hashable). By default these are empty, but can be added or changed using direct manipulation of the attribute dictionaries named graph, node and edge respectively.

G.graph[“day”] = boost::any(“Friday”);

{‘day’: ‘Friday’}

Subclasses (Advanced):**

The DiGraphS class uses a container-of-container-of-container data structure. The outer dict (node_dict) holds adjacency information keyed by node. The next dict (adjlist_dict) represents the adjacency information and holds edge data keyed by neighbor. The inner dict (edge_attr_dict) represents the edge data and holds edge attribute values keyed by attribute names.

Each of these three dicts can be replaced in a subclass by a user defined dict-like object. In general, the dict-like features should be maintained but extra features can be added. To replace one of the dicts create a new graph class by changing the class(!) variable holding the factory for that dict-like structure. The variable names are node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory, adjlist_outer_dict_factory, edge_attr_dict_factory and graph_attr_dict_factory.

node_dict_factory : function, (default: dict) Factory function to be used to create the dict containing node attributes, keyed by node id. It should require no arguments and return a dict-like object

node_attr_dict_factory: function, (default: dict) Factory function to be used to create the node attribute dict which holds attribute values keyed by attribute name. It should require no arguments and return a dict-like object

adjlist_outer_dict_factory : function, (default: dict) Factory function to be used to create the outer-most dict in the data structure that holds adjacency info keyed by node. It should require no arguments and return a dict-like object.

adjlist_inner_dict_factory : function, (default: dict) Factory function to be used to create the adjacency list dict which holds edge data keyed by neighbor. It should require no arguments and return a dict-like object

edge_attr_dict_factory : function, (default: dict) Factory function to be used to create the edge attribute dict which holds attribute values keyed by attribute name. It should require no arguments and return a dict-like object.

graph_attr_dict_factory : function, (default: dict) Factory function to be used to create the graph attribute dict which holds attribute values keyed by attribute name. It should require no arguments and return a dict-like object.

Typically, if your extension doesn’t impact the data structure all methods will inherit without issue except: to_directed/to_undirected. By default these methods create a DiGraph/DiGraphS class and you probably want them to create your extension of a DiGraph/DiGraphS. To facilitate this we define two class variables that you can set in your subclass.

to_directed_class : callable, (default: DiGraph or MultiDiGraph) Class to create a new graph structure in the to_directed method. If None, a NetworkX class (DiGraph or MultiDiGraph) is used.

to_undirected_class : callable, (default: DiGraphS or MultiGraph) Class to create a new graph structure in the to_undirected method. If None, a NetworkX class (DiGraphS or MultiGraph) is used.

Examples

Create a low memory graph class that effectively disallows edge attributes by using a single attribute dict for all edges. This reduces the memory used, but you lose edge attributes.

class ThinGraph(xn::DiGraphS):

… all_edge_dict = {‘weight’: 1} … def single_edge_dict(self): … return self.all_edge_dict … edge_attr_dict_factory = single_edge_dict

G = ThinGraph() G.add_edge(2, 1) G[2][1]

{‘weight’: 1}

G.add_edge(2, 2) G[2][1] is G[2][2]

True

Please see :mod:~networkx.classes.ordered for more examples of creating graph subclasses by overwriting the base class dict with a dictionary-like object.

Public Types

using Node = typename _Base::Node
using edge_t = std::pair<Node, Node>
using graph_attr_dict_factory = typename _Base::graph_attr_dict_factory
using adjlist_outer_dict_factory = typename _Base::adjlist_outer_dict_factory
using key_type = typename _Base::key_type
using value_type = typename _Base::value_type
using coro_t = boost::coroutines2::coroutine<edge_t>
using pull_t = typename coro_t::pull_type

Public Functions

inline explicit DiGraphS(const nodeview_t &Nodes)

Initialize a graph with edges, name, or graph attributes.

Parameters

node_container : input nodes

Examples

v = std::vector{5, 3, 2}; G = xn::DiGraphS(v); // or DiGraph, MultiGraph, MultiDiGraph, etc

r = py::range(100); G = xn::DiGraphS(r, r); // or DiGraph, MultiGraph, MultiDiGraph,

etc

inline explicit DiGraphS(int num_nodes)
inline auto adj() const

DiGraphS adjacency object holding the neighbors of each node.

This object is a read-only dict-like structure with node keys and neighbor-dict values. The neighbor-dict is keyed by neighbor to the edge-data-dict. So `G.adj[3][2][‘color’] = ‘blue’sets the color of the edge(3, 2)to”blue”`.

Iterating over G.adj behaves like a dict. Useful idioms include for nbr, datadict in G.adj[n].items():.

The neighbor information is also provided by subscripting the graph. So `for nbr, foovalue in G[node].data(‘foo’, default=1):` works.

For directed graphs, G.adj holds outgoing (successor) info.

inline auto succ() const

Graph adjacency object holding the successors of each node.

This object is a read-only dict-like structure with node keys and neighbor-dict values. The neighbor-dict is keyed by neighbor to the edge-data-dict. So `G.succ[3][2][‘color’] = ‘blue’sets the color of the edge(3, 2)to”blue”`.

Iterating over G.succ behaves like a dict. Useful idioms include for nbr, datadict in G.succ[n].items():. A data-view not provided by dicts also exists: `for nbr, foovalue in G.succ[node].data(‘foo’): and a default can be set via adefaultargument to thedata` method.

The neighbor information is also provided by subscripting the graph. So `for nbr, foovalue in G[node].data(‘foo’, default=1):` works.

For directed graphs, G.adj is identical to G.succ.

template<typename U = key_type>
inline std::enable_if<std::is_same<U, value_type>::value>::type add_edge(const Node &u, const Node &v)

Add an edge between u and v.

The nodes u and v will be automatically added if (they are not already : the graph.

Edge attributes can be specified with keywords or by directly accessing the edge”s attribute dictionary. See examples below.

Parameters

u, v : nodes Nodes can be, for example, strings or numbers. Nodes must be hashable (and not None) C++ objects.

See Also

add_edges_from : add a collection of edges

Notes

Adding an edge that already exists updates the edge data.

Many XNetwork algorithms designed for weighted graphs use an edge attribute (by default weight) to hold a numerical value.

Examples

The following all add the edge e=(1, 2) to graph G) {

G = xn::DiGraphS() // or DiGraph, MultiGraph, MultiDiGraph, etc e = (1, 2); G.add_edge(1, 2) // explicit two-node form G.add_edges_from([(1, 2)]); // add edges from iterable container

Associate data to edges using keywords) {

G.add_edge(1, 2);

For non-string attribute keys, use subscript notation.

G.add_edge(1, 2); G[1][2].update({0: 5}); G.edges()[1, 2].update({0: 5});

template<typename U = key_type>
inline std::enable_if<!std::is_same<U, value_type>::value>::type add_edge(const Node &u, const Node &v)
template<typename T>
inline auto add_edge(const Node &u, const Node &v, const T &data)
template<typename C1, typename C2>
inline auto add_edges_from(const C1 &edges, const C2 &data)
inline auto has_successor(const Node &u, const Node &v) -> bool

Returns True if node u has successor v.

This is true if graph has the edge u->v.

inline auto &successors(const Node &n)

Returns an iterator over successor nodes of n.

A successor of n is a node m such that there exists a directed edge from n to m.

Parameters

n : node A node in the graph

Raises

NetworkXError If n is not in the graph.

See Also

predecessors

Notes

neighbors() and successors() are the same.

inline const auto &successors(const Node &n) const
inline auto edges() const -> pull_t

An OutEdgeView of the DiGraph as G.edges().

edges(self, nbunch=None, data=False, default=None)

The OutEdgeView provides set-like operations on the edge-tuples as well as edge attribute lookup. When called, it also provides an EdgeDataView object which allows control of access to edge attributes (but does not provide set-like operations). Hence, `G.edges()[u, v][‘color’]provides the value of the color attribute for edge(u, v)while for (u, v, c) in G.edges().data(‘color’, default=’red’):` iterates through all the edges yielding the color attribute with default 'red' if no color attribute exists.

Parameters

nbunch : single node, container, or all nodes (default= all nodes) The view will only report edges incident to these nodes. data : string or bool, optional (default=False) The edge attribute returned in 3-tuple (u, v, ddict[data]). If True, return edge attribute dict in 3-tuple (u, v, ddict). If False, return 2-tuple (u, v). default : value, optional (default=None) Value used for edges that don’t have the requested attribute. Only relevant if data is not True or False.

Returns

edges : OutEdgeView A view of edge attributes, usually it iterates over (u, v) or (u, v, d) tuples of edges, but can also be used for attribute lookup as `edges[u, v][‘foo’]`.

See Also

in_edges, out_edges

Notes

Nodes in nbunch that are not in the graph will be (quietly) ignored. For directed graphs this returns the out-edges.

Examples

G = nx.DiGraph() # or MultiDiGraph, etc nx.add_path(G, [0, 1, 2]) G.add_edge(2, 3, weight=5) [e for e in G.edges()]

[(0, 1), (1, 2), (2, 3)]

G.edges().data() # default data is {} (empty dict)

OutEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {‘weight’: 5})])

G.edges().data(‘weight’, default=1)

OutEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])

G.edges()([0, 2]) # only edges incident to these nodes

OutEdgeDataView([(0, 1), (2, 3)])

G.edges()(0) # only edges incident to a single node (use G.adj[0]?)

OutEdgeDataView([(0, 1)])

inline auto degree(const Node &n) const
inline auto clear()

Remove all nodes and edges from the graph.

This also removes the name, and all graph, node, and edge attributes.

Examples

G = xn::path_graph(4); // or DiGraph, MultiGraph, MultiDiGraph, etc G.clear(); list(G.nodes);

[];

list(G.edges());

[];

inline auto is_multigraph()

Return true if (graph is a multigraph, false otherwise.

inline auto is_directed()

Return true if (graph is directed, false otherwise.

Public Members

adjlist_outer_dict_factory &_succ