914 lines
33 KiB
Python
914 lines
33 KiB
Python
"""Base class for MultiDiGraph."""
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from copy import deepcopy
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import networkx as nx
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from networkx.classes.digraph import DiGraph
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from networkx.classes.multigraph import MultiGraph
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from networkx.classes.coreviews import MultiAdjacencyView
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from networkx.classes.reportviews import (
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OutMultiEdgeView,
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InMultiEdgeView,
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DiMultiDegreeView,
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OutMultiDegreeView,
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InMultiDegreeView,
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)
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from networkx.exception import NetworkXError
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import networkx.convert as convert
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__all__ = ["MultiDiGraph"]
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class MultiDiGraph(MultiGraph, DiGraph):
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"""A directed graph class that can store multiedges.
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Multiedges are multiple edges between two nodes. Each edge
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can hold optional data or attributes.
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A MultiDiGraph holds directed edges. Self loops are allowed.
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Nodes can be arbitrary (hashable) Python objects with optional
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key/value attributes. By convention `None` is not used as a node.
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Edges are represented as links between nodes with optional
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key/value attributes.
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Parameters
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----------
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incoming_graph_data : input graph (optional, default: None)
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Data to initialize graph. If None (default) an empty
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graph is created. The data can be any format that is supported
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by the to_networkx_graph() function, currently including edge list,
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dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy
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sparse matrix, or PyGraphviz graph.
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multigraph_input : bool or None (default None)
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Note: Only used when `incoming_graph_data` is a dict.
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If True, `incoming_graph_data` is assumed to be a
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dict-of-dict-of-dict-of-dict structure keyed by
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node to neighbor to edge keys to edge data for multi-edges.
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A NetworkXError is raised if this is not the case.
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If False, :func:`to_networkx_graph` is used to try to determine
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the dict's graph data structure as either a dict-of-dict-of-dict
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keyed by node to neighbor to edge data, or a dict-of-iterable
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keyed by node to neighbors.
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If None, the treatment for True is tried, but if it fails,
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the treatment for False is tried.
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attr : keyword arguments, optional (default= no attributes)
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Attributes to add to graph as key=value pairs.
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See Also
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--------
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Graph
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DiGraph
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MultiGraph
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OrderedMultiDiGraph
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Examples
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--------
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Create an empty graph structure (a "null graph") with no nodes and
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no edges.
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>>> G = nx.MultiDiGraph()
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G can be grown in several ways.
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**Nodes:**
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Add one node at a time:
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>>> G.add_node(1)
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Add the nodes from any container (a list, dict, set or
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even the lines from a file or the nodes from another graph).
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>>> G.add_nodes_from([2, 3])
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>>> G.add_nodes_from(range(100, 110))
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>>> H = nx.path_graph(10)
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>>> G.add_nodes_from(H)
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In addition to strings and integers any hashable Python object
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(except None) can represent a node, e.g. a customized node object,
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or even another Graph.
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>>> G.add_node(H)
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**Edges:**
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G can also be grown by adding edges.
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Add one edge,
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>>> key = G.add_edge(1, 2)
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a list of edges,
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>>> keys = G.add_edges_from([(1, 2), (1, 3)])
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or a collection of edges,
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>>> keys = G.add_edges_from(H.edges)
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If some edges connect nodes not yet in the graph, the nodes
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are added automatically. If an edge already exists, an additional
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edge is created and stored using a key to identify the edge.
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By default the key is the lowest unused integer.
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>>> keys = G.add_edges_from([(4, 5, dict(route=282)), (4, 5, dict(route=37))])
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>>> G[4]
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AdjacencyView({5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}})
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**Attributes:**
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Each graph, node, and edge can hold key/value attribute pairs
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in an associated attribute dictionary (the keys must be hashable).
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By default these are empty, but can be added or changed using
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add_edge, add_node or direct manipulation of the attribute
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dictionaries named graph, node and edge respectively.
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>>> G = nx.MultiDiGraph(day="Friday")
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>>> G.graph
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{'day': 'Friday'}
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Add node attributes using add_node(), add_nodes_from() or G.nodes
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>>> G.add_node(1, time="5pm")
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>>> G.add_nodes_from([3], time="2pm")
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>>> G.nodes[1]
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{'time': '5pm'}
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>>> G.nodes[1]["room"] = 714
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>>> del G.nodes[1]["room"] # remove attribute
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>>> list(G.nodes(data=True))
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[(1, {'time': '5pm'}), (3, {'time': '2pm'})]
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Add edge attributes using add_edge(), add_edges_from(), subscript
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notation, or G.edges.
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>>> key = G.add_edge(1, 2, weight=4.7)
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>>> keys = G.add_edges_from([(3, 4), (4, 5)], color="red")
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>>> keys = G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
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>>> G[1][2][0]["weight"] = 4.7
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>>> G.edges[1, 2, 0]["weight"] = 4
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Warning: we protect the graph data structure by making `G.edges[1, 2]` a
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read-only dict-like structure. However, you can assign to attributes
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in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
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data attributes: `G.edges[1, 2]['weight'] = 4`
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(For multigraphs: `MG.edges[u, v, key][name] = value`).
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**Shortcuts:**
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Many common graph features allow python syntax to speed reporting.
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>>> 1 in G # check if node in graph
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True
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>>> [n for n in G if n < 3] # iterate through nodes
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[1, 2]
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>>> len(G) # number of nodes in graph
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5
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>>> G[1] # adjacency dict-like view keyed by neighbor to edge attributes
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AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
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Often the best way to traverse all edges of a graph is via the neighbors.
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The neighbors are available as an adjacency-view `G.adj` object or via
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the method `G.adjacency()`.
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>>> for n, nbrsdict in G.adjacency():
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... for nbr, keydict in nbrsdict.items():
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... for key, eattr in keydict.items():
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... if "weight" in eattr:
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... # Do something useful with the edges
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... pass
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But the edges() method is often more convenient:
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>>> for u, v, keys, weight in G.edges(data="weight", keys=True):
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... if weight is not None:
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... # Do something useful with the edges
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... pass
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**Reporting:**
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Simple graph information is obtained using methods and object-attributes.
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Reporting usually provides views instead of containers to reduce memory
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usage. The views update as the graph is updated similarly to dict-views.
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The objects `nodes, `edges` and `adj` provide access to data attributes
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via lookup (e.g. `nodes[n], `edges[u, v]`, `adj[u][v]`) and iteration
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(e.g. `nodes.items()`, `nodes.data('color')`,
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`nodes.data('color', default='blue')` and similarly for `edges`)
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Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
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For details on these and other miscellaneous methods, see below.
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**Subclasses (Advanced):**
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The MultiDiGraph class uses a dict-of-dict-of-dict-of-dict structure.
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The outer dict (node_dict) holds adjacency information keyed by node.
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The next dict (adjlist_dict) represents the adjacency information and holds
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edge_key dicts keyed by neighbor. The edge_key dict holds each edge_attr
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dict keyed by edge key. The inner dict (edge_attr_dict) represents
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the edge data and holds edge attribute values keyed by attribute names.
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Each of these four dicts in the dict-of-dict-of-dict-of-dict
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structure can be replaced by a user defined dict-like object.
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In general, the dict-like features should be maintained but
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extra features can be added. To replace one of the dicts create
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a new graph class by changing the class(!) variable holding the
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factory for that dict-like structure. The variable names are
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node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
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adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
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and graph_attr_dict_factory.
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node_dict_factory : function, (default: dict)
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Factory function to be used to create the dict containing node
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attributes, keyed by node id.
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It should require no arguments and return a dict-like object
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node_attr_dict_factory: function, (default: dict)
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Factory function to be used to create the node attribute
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dict which holds attribute values keyed by attribute name.
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It should require no arguments and return a dict-like object
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adjlist_outer_dict_factory : function, (default: dict)
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Factory function to be used to create the outer-most dict
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in the data structure that holds adjacency info keyed by node.
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It should require no arguments and return a dict-like object.
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adjlist_inner_dict_factory : function, (default: dict)
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Factory function to be used to create the adjacency list
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dict which holds multiedge key dicts keyed by neighbor.
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It should require no arguments and return a dict-like object.
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edge_key_dict_factory : function, (default: dict)
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Factory function to be used to create the edge key dict
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which holds edge data keyed by edge key.
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It should require no arguments and return a dict-like object.
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edge_attr_dict_factory : function, (default: dict)
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Factory function to be used to create the edge attribute
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dict which holds attribute values keyed by attribute name.
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It should require no arguments and return a dict-like object.
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graph_attr_dict_factory : function, (default: dict)
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Factory function to be used to create the graph attribute
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dict which holds attribute values keyed by attribute name.
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It should require no arguments and return a dict-like object.
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Typically, if your extension doesn't impact the data structure all
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methods will inherited without issue except: `to_directed/to_undirected`.
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By default these methods create a DiGraph/Graph class and you probably
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want them to create your extension of a DiGraph/Graph. To facilitate
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this we define two class variables that you can set in your subclass.
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to_directed_class : callable, (default: DiGraph or MultiDiGraph)
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Class to create a new graph structure in the `to_directed` method.
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If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
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to_undirected_class : callable, (default: Graph or MultiGraph)
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Class to create a new graph structure in the `to_undirected` method.
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If `None`, a NetworkX class (Graph or MultiGraph) is used.
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Please see :mod:`~networkx.classes.ordered` for examples of
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creating graph subclasses by overwriting the base class `dict` with
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a dictionary-like object.
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"""
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# node_dict_factory = dict # already assigned in Graph
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# adjlist_outer_dict_factory = dict
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# adjlist_inner_dict_factory = dict
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edge_key_dict_factory = dict
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# edge_attr_dict_factory = dict
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def __init__(self, incoming_graph_data=None, multigraph_input=None, **attr):
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"""Initialize a graph with edges, name, or graph attributes.
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Parameters
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----------
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incoming_graph_data : input graph
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Data to initialize graph. If incoming_graph_data=None (default)
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an empty graph is created. The data can be an edge list, or any
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NetworkX graph object. If the corresponding optional Python
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packages are installed the data can also be a 2D NumPy array, a
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SciPy sparse matrix, or a PyGraphviz graph.
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multigraph_input : bool or None (default None)
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Note: Only used when `incoming_graph_data` is a dict.
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If True, `incoming_graph_data` is assumed to be a
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dict-of-dict-of-dict-of-dict structure keyed by
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node to neighbor to edge keys to edge data for multi-edges.
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A NetworkXError is raised if this is not the case.
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If False, :func:`to_networkx_graph` is used to try to determine
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the dict's graph data structure as either a dict-of-dict-of-dict
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keyed by node to neighbor to edge data, or a dict-of-iterable
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keyed by node to neighbors.
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If None, the treatment for True is tried, but if it fails,
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the treatment for False is tried.
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attr : keyword arguments, optional (default= no attributes)
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Attributes to add to graph as key=value pairs.
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See Also
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--------
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convert
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Examples
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--------
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>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
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>>> G = nx.Graph(name="my graph")
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>>> e = [(1, 2), (2, 3), (3, 4)] # list of edges
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>>> G = nx.Graph(e)
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Arbitrary graph attribute pairs (key=value) may be assigned
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>>> G = nx.Graph(e, day="Friday")
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>>> G.graph
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{'day': 'Friday'}
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"""
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self.edge_key_dict_factory = self.edge_key_dict_factory
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# multigraph_input can be None/True/False. So check "is not False"
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if isinstance(incoming_graph_data, dict) and multigraph_input is not False:
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DiGraph.__init__(self)
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try:
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convert.from_dict_of_dicts(
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incoming_graph_data, create_using=self, multigraph_input=True
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)
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self.graph.update(attr)
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except Exception as err:
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if multigraph_input is True:
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raise nx.NetworkXError(
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f"converting multigraph_input raised:\n{type(err)}: {err}"
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)
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DiGraph.__init__(self, incoming_graph_data, **attr)
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else:
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DiGraph.__init__(self, incoming_graph_data, **attr)
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@property
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def adj(self):
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"""Graph adjacency object holding the neighbors of each node.
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This object is a read-only dict-like structure with node keys
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and neighbor-dict values. The neighbor-dict is keyed by neighbor
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to the edgekey-dict. So `G.adj[3][2][0]['color'] = 'blue'` sets
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the color of the edge `(3, 2, 0)` to `"blue"`.
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Iterating over G.adj behaves like a dict. Useful idioms include
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`for nbr, datadict in G.adj[n].items():`.
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The neighbor information is also provided by subscripting the graph.
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So `for nbr, foovalue in G[node].data('foo', default=1):` works.
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For directed graphs, `G.adj` holds outgoing (successor) info.
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"""
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return MultiAdjacencyView(self._succ)
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@property
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def succ(self):
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"""Graph adjacency object holding the successors of each node.
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This object is a read-only dict-like structure with node keys
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and neighbor-dict values. The neighbor-dict is keyed by neighbor
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to the edgekey-dict. So `G.adj[3][2][0]['color'] = 'blue'` sets
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the color of the edge `(3, 2, 0)` to `"blue"`.
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Iterating over G.adj behaves like a dict. Useful idioms include
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`for nbr, datadict in G.adj[n].items():`.
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The neighbor information is also provided by subscripting the graph.
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So `for nbr, foovalue in G[node].data('foo', default=1):` works.
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For directed graphs, `G.succ` is identical to `G.adj`.
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"""
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return MultiAdjacencyView(self._succ)
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@property
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def pred(self):
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"""Graph adjacency object holding the predecessors of each node.
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This object is a read-only dict-like structure with node keys
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and neighbor-dict values. The neighbor-dict is keyed by neighbor
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to the edgekey-dict. So `G.adj[3][2][0]['color'] = 'blue'` sets
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the color of the edge `(3, 2, 0)` to `"blue"`.
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Iterating over G.adj behaves like a dict. Useful idioms include
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`for nbr, datadict in G.adj[n].items():`.
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"""
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return MultiAdjacencyView(self._pred)
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def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
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"""Add an edge between u and v.
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The nodes u and v will be automatically added if they are
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not already in the graph.
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Edge attributes can be specified with keywords or by directly
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accessing the edge's attribute dictionary. See examples below.
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Parameters
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----------
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u_for_edge, v_for_edge : nodes
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Nodes can be, for example, strings or numbers.
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Nodes must be hashable (and not None) Python objects.
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key : hashable identifier, optional (default=lowest unused integer)
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Used to distinguish multiedges between a pair of nodes.
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attr : keyword arguments, optional
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Edge data (or labels or objects) can be assigned using
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keyword arguments.
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Returns
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-------
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The edge key assigned to the edge.
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See Also
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--------
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add_edges_from : add a collection of edges
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Notes
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-----
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To replace/update edge data, use the optional key argument
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to identify a unique edge. Otherwise a new edge will be created.
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NetworkX algorithms designed for weighted graphs cannot use
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multigraphs directly because it is not clear how to handle
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multiedge weights. Convert to Graph using edge attribute
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'weight' to enable weighted graph algorithms.
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Default keys are generated using the method `new_edge_key()`.
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This method can be overridden by subclassing the base class and
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providing a custom `new_edge_key()` method.
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Examples
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--------
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The following all add the edge e=(1, 2) to graph G:
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>>> G = nx.MultiDiGraph()
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>>> e = (1, 2)
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>>> key = G.add_edge(1, 2) # explicit two-node form
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>>> G.add_edge(*e) # single edge as tuple of two nodes
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1
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>>> G.add_edges_from([(1, 2)]) # add edges from iterable container
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[2]
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Associate data to edges using keywords:
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>>> key = G.add_edge(1, 2, weight=3)
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>>> key = G.add_edge(1, 2, key=0, weight=4) # update data for key=0
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>>> key = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
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For non-string attribute keys, use subscript notation.
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>>> ekey = G.add_edge(1, 2)
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>>> G[1][2][0].update({0: 5})
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>>> G.edges[1, 2, 0].update({0: 5})
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"""
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u, v = u_for_edge, v_for_edge
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# add nodes
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if u not in self._succ:
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if u is None:
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raise ValueError("None cannot be a node")
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self._succ[u] = self.adjlist_inner_dict_factory()
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self._pred[u] = self.adjlist_inner_dict_factory()
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self._node[u] = self.node_attr_dict_factory()
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if v not in self._succ:
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if v is None:
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raise ValueError("None cannot be a node")
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self._succ[v] = self.adjlist_inner_dict_factory()
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self._pred[v] = self.adjlist_inner_dict_factory()
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self._node[v] = self.node_attr_dict_factory()
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if key is None:
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key = self.new_edge_key(u, v)
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if v in self._succ[u]:
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keydict = self._adj[u][v]
|
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datadict = keydict.get(key, self.edge_key_dict_factory())
|
|
datadict.update(attr)
|
|
keydict[key] = datadict
|
|
else:
|
|
# selfloops work this way without special treatment
|
|
datadict = self.edge_attr_dict_factory()
|
|
datadict.update(attr)
|
|
keydict = self.edge_key_dict_factory()
|
|
keydict[key] = datadict
|
|
self._succ[u][v] = keydict
|
|
self._pred[v][u] = keydict
|
|
return key
|
|
|
|
def remove_edge(self, u, v, key=None):
|
|
"""Remove an edge between u and v.
|
|
|
|
Parameters
|
|
----------
|
|
u, v : nodes
|
|
Remove an edge between nodes u and v.
|
|
key : hashable identifier, optional (default=None)
|
|
Used to distinguish multiple edges between a pair of nodes.
|
|
If None remove a single (arbitrary) edge between u and v.
|
|
|
|
Raises
|
|
------
|
|
NetworkXError
|
|
If there is not an edge between u and v, or
|
|
if there is no edge with the specified key.
|
|
|
|
See Also
|
|
--------
|
|
remove_edges_from : remove a collection of edges
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.MultiDiGraph()
|
|
>>> nx.add_path(G, [0, 1, 2, 3])
|
|
>>> G.remove_edge(0, 1)
|
|
>>> e = (1, 2)
|
|
>>> G.remove_edge(*e) # unpacks e from an edge tuple
|
|
|
|
For multiple edges
|
|
|
|
>>> G = nx.MultiDiGraph()
|
|
>>> G.add_edges_from([(1, 2), (1, 2), (1, 2)]) # key_list returned
|
|
[0, 1, 2]
|
|
>>> G.remove_edge(1, 2) # remove a single (arbitrary) edge
|
|
|
|
For edges with keys
|
|
|
|
>>> G = nx.MultiDiGraph()
|
|
>>> G.add_edge(1, 2, key="first")
|
|
'first'
|
|
>>> G.add_edge(1, 2, key="second")
|
|
'second'
|
|
>>> G.remove_edge(1, 2, key="second")
|
|
|
|
"""
|
|
try:
|
|
d = self._adj[u][v]
|
|
except KeyError as err:
|
|
raise NetworkXError(f"The edge {u}-{v} is not in the graph.") from err
|
|
# remove the edge with specified data
|
|
if key is None:
|
|
d.popitem()
|
|
else:
|
|
try:
|
|
del d[key]
|
|
except KeyError as err:
|
|
msg = f"The edge {u}-{v} with key {key} is not in the graph."
|
|
raise NetworkXError(msg) from err
|
|
if len(d) == 0:
|
|
# remove the key entries if last edge
|
|
del self._succ[u][v]
|
|
del self._pred[v][u]
|
|
|
|
@property
|
|
def edges(self):
|
|
"""An OutMultiEdgeView of the Graph as G.edges or G.edges().
|
|
|
|
edges(self, nbunch=None, data=False, keys=False, default=None)
|
|
|
|
The OutMultiEdgeView 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.
|
|
|
|
Edges are returned as tuples with optional data and keys
|
|
in the order (node, neighbor, key, data).
|
|
|
|
Parameters
|
|
----------
|
|
nbunch : single node, container, or all nodes (default= all nodes)
|
|
The view will only report edges from 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).
|
|
keys : bool, optional (default=False)
|
|
If True, return edge keys with each edge.
|
|
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 : EdgeView
|
|
A view of edge attributes, usually it iterates over (u, v)
|
|
(u, v, k) or (u, v, k, d) tuples of edges, but can also be
|
|
used for attribute lookup as `edges[u, v, k]['foo']`.
|
|
|
|
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.MultiDiGraph()
|
|
>>> nx.add_path(G, [0, 1, 2])
|
|
>>> key = G.add_edge(2, 3, weight=5)
|
|
>>> [e for e in G.edges()]
|
|
[(0, 1), (1, 2), (2, 3)]
|
|
>>> list(G.edges(data=True)) # default data is {} (empty dict)
|
|
[(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]
|
|
>>> list(G.edges(data="weight", default=1))
|
|
[(0, 1, 1), (1, 2, 1), (2, 3, 5)]
|
|
>>> list(G.edges(keys=True)) # default keys are integers
|
|
[(0, 1, 0), (1, 2, 0), (2, 3, 0)]
|
|
>>> list(G.edges(data=True, keys=True))
|
|
[(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {'weight': 5})]
|
|
>>> list(G.edges(data="weight", default=1, keys=True))
|
|
[(0, 1, 0, 1), (1, 2, 0, 1), (2, 3, 0, 5)]
|
|
>>> list(G.edges([0, 2]))
|
|
[(0, 1), (2, 3)]
|
|
>>> list(G.edges(0))
|
|
[(0, 1)]
|
|
|
|
See Also
|
|
--------
|
|
in_edges, out_edges
|
|
"""
|
|
return OutMultiEdgeView(self)
|
|
|
|
# alias out_edges to edges
|
|
out_edges = edges
|
|
|
|
@property
|
|
def in_edges(self):
|
|
"""An InMultiEdgeView of the Graph as G.in_edges or G.in_edges().
|
|
|
|
in_edges(self, nbunch=None, data=False, keys=False, default=None)
|
|
|
|
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).
|
|
keys : bool, optional (default=False)
|
|
If True, return edge keys with each edge.
|
|
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
|
|
-------
|
|
in_edges : InMultiEdgeView
|
|
A view of edge attributes, usually it iterates over (u, v)
|
|
or (u, v, k) or (u, v, k, d) tuples of edges, but can also be
|
|
used for attribute lookup as `edges[u, v, k]['foo']`.
|
|
|
|
See Also
|
|
--------
|
|
edges
|
|
"""
|
|
return InMultiEdgeView(self)
|
|
|
|
@property
|
|
def degree(self):
|
|
"""A DegreeView for the Graph as G.degree or G.degree().
|
|
|
|
The node degree is the number of edges adjacent to the node.
|
|
The weighted node degree is the sum of the edge weights for
|
|
edges incident to that node.
|
|
|
|
This object provides an iterator for (node, degree) as well as
|
|
lookup for the degree for a single node.
|
|
|
|
Parameters
|
|
----------
|
|
nbunch : single node, container, or all nodes (default= all nodes)
|
|
The view will only report edges incident to these nodes.
|
|
|
|
weight : string or None, optional (default=None)
|
|
The name of an edge attribute that holds the numerical value used
|
|
as a weight. If None, then each edge has weight 1.
|
|
The degree is the sum of the edge weights adjacent to the node.
|
|
|
|
Returns
|
|
-------
|
|
If a single nodes is requested
|
|
deg : int
|
|
Degree of the node
|
|
|
|
OR if multiple nodes are requested
|
|
nd_iter : iterator
|
|
The iterator returns two-tuples of (node, degree).
|
|
|
|
See Also
|
|
--------
|
|
out_degree, in_degree
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.MultiDiGraph()
|
|
>>> nx.add_path(G, [0, 1, 2, 3])
|
|
>>> G.degree(0) # node 0 with degree 1
|
|
1
|
|
>>> list(G.degree([0, 1, 2]))
|
|
[(0, 1), (1, 2), (2, 2)]
|
|
|
|
"""
|
|
return DiMultiDegreeView(self)
|
|
|
|
@property
|
|
def in_degree(self):
|
|
"""A DegreeView for (node, in_degree) or in_degree for single node.
|
|
|
|
The node in-degree is the number of edges pointing in to the node.
|
|
The weighted node degree is the sum of the edge weights for
|
|
edges incident to that node.
|
|
|
|
This object provides an iterator for (node, degree) as well as
|
|
lookup for the degree for a single node.
|
|
|
|
Parameters
|
|
----------
|
|
nbunch : single node, container, or all nodes (default= all nodes)
|
|
The view will only report edges incident to these nodes.
|
|
|
|
weight : string or None, optional (default=None)
|
|
The edge attribute that holds the numerical value used
|
|
as a weight. If None, then each edge has weight 1.
|
|
The degree is the sum of the edge weights adjacent to the node.
|
|
|
|
Returns
|
|
-------
|
|
If a single node is requested
|
|
deg : int
|
|
Degree of the node
|
|
|
|
OR if multiple nodes are requested
|
|
nd_iter : iterator
|
|
The iterator returns two-tuples of (node, in-degree).
|
|
|
|
See Also
|
|
--------
|
|
degree, out_degree
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.MultiDiGraph()
|
|
>>> nx.add_path(G, [0, 1, 2, 3])
|
|
>>> G.in_degree(0) # node 0 with degree 0
|
|
0
|
|
>>> list(G.in_degree([0, 1, 2]))
|
|
[(0, 0), (1, 1), (2, 1)]
|
|
|
|
"""
|
|
return InMultiDegreeView(self)
|
|
|
|
@property
|
|
def out_degree(self):
|
|
"""Returns an iterator for (node, out-degree) or out-degree for single node.
|
|
|
|
out_degree(self, nbunch=None, weight=None)
|
|
|
|
The node out-degree is the number of edges pointing out of the node.
|
|
This function returns the out-degree for a single node or an iterator
|
|
for a bunch of nodes or if nothing is passed as argument.
|
|
|
|
Parameters
|
|
----------
|
|
nbunch : single node, container, or all nodes (default= all nodes)
|
|
The view will only report edges incident to these nodes.
|
|
|
|
weight : string or None, optional (default=None)
|
|
The edge attribute that holds the numerical value used
|
|
as a weight. If None, then each edge has weight 1.
|
|
The degree is the sum of the edge weights.
|
|
|
|
Returns
|
|
-------
|
|
If a single node is requested
|
|
deg : int
|
|
Degree of the node
|
|
|
|
OR if multiple nodes are requested
|
|
nd_iter : iterator
|
|
The iterator returns two-tuples of (node, out-degree).
|
|
|
|
See Also
|
|
--------
|
|
degree, in_degree
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.MultiDiGraph()
|
|
>>> nx.add_path(G, [0, 1, 2, 3])
|
|
>>> G.out_degree(0) # node 0 with degree 1
|
|
1
|
|
>>> list(G.out_degree([0, 1, 2]))
|
|
[(0, 1), (1, 1), (2, 1)]
|
|
|
|
"""
|
|
return OutMultiDegreeView(self)
|
|
|
|
def is_multigraph(self):
|
|
"""Returns True if graph is a multigraph, False otherwise."""
|
|
return True
|
|
|
|
def is_directed(self):
|
|
"""Returns True if graph is directed, False otherwise."""
|
|
return True
|
|
|
|
def to_undirected(self, reciprocal=False, as_view=False):
|
|
"""Returns an undirected representation of the digraph.
|
|
|
|
Parameters
|
|
----------
|
|
reciprocal : bool (optional)
|
|
If True only keep edges that appear in both directions
|
|
in the original digraph.
|
|
as_view : bool (optional, default=False)
|
|
If True return an undirected view of the original directed graph.
|
|
|
|
Returns
|
|
-------
|
|
G : MultiGraph
|
|
An undirected graph with the same name and nodes and
|
|
with edge (u, v, data) if either (u, v, data) or (v, u, data)
|
|
is in the digraph. If both edges exist in digraph and
|
|
their edge data is different, only one edge is created
|
|
with an arbitrary choice of which edge data to use.
|
|
You must check and correct for this manually if desired.
|
|
|
|
See Also
|
|
--------
|
|
MultiGraph, copy, add_edge, add_edges_from
|
|
|
|
Notes
|
|
-----
|
|
This returns a "deepcopy" of the edge, node, and
|
|
graph attributes which attempts to completely copy
|
|
all of the data and references.
|
|
|
|
This is in contrast to the similar D=MultiDiGraph(G) which
|
|
returns a shallow copy of the data.
|
|
|
|
See the Python copy module for more information on shallow
|
|
and deep copies, https://docs.python.org/3/library/copy.html.
|
|
|
|
Warning: If you have subclassed MultiDiGraph to use dict-like
|
|
objects in the data structure, those changes do not transfer
|
|
to the MultiGraph created by this method.
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.path_graph(2) # or MultiGraph, etc
|
|
>>> H = G.to_directed()
|
|
>>> list(H.edges)
|
|
[(0, 1), (1, 0)]
|
|
>>> G2 = H.to_undirected()
|
|
>>> list(G2.edges)
|
|
[(0, 1)]
|
|
"""
|
|
graph_class = self.to_undirected_class()
|
|
if as_view is True:
|
|
return nx.graphviews.generic_graph_view(self, graph_class)
|
|
# deepcopy when not a view
|
|
G = graph_class()
|
|
G.graph.update(deepcopy(self.graph))
|
|
G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
|
|
if reciprocal is True:
|
|
G.add_edges_from(
|
|
(u, v, key, deepcopy(data))
|
|
for u, nbrs in self._adj.items()
|
|
for v, keydict in nbrs.items()
|
|
for key, data in keydict.items()
|
|
if v in self._pred[u] and key in self._pred[u][v]
|
|
)
|
|
else:
|
|
G.add_edges_from(
|
|
(u, v, key, deepcopy(data))
|
|
for u, nbrs in self._adj.items()
|
|
for v, keydict in nbrs.items()
|
|
for key, data in keydict.items()
|
|
)
|
|
return G
|
|
|
|
def reverse(self, copy=True):
|
|
"""Returns the reverse of the graph.
|
|
|
|
The reverse is a graph with the same nodes and edges
|
|
but with the directions of the edges reversed.
|
|
|
|
Parameters
|
|
----------
|
|
copy : bool optional (default=True)
|
|
If True, return a new DiGraph holding the reversed edges.
|
|
If False, the reverse graph is created using a view of
|
|
the original graph.
|
|
"""
|
|
if copy:
|
|
H = self.__class__()
|
|
H.graph.update(deepcopy(self.graph))
|
|
H.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
|
|
H.add_edges_from(
|
|
(v, u, k, deepcopy(d))
|
|
for u, v, k, d in self.edges(keys=True, data=True)
|
|
)
|
|
return H
|
|
return nx.graphviews.reverse_view(self)
|