Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
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1219 lines
42 KiB
1219 lines
42 KiB
"""Base class for directed graphs."""
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from copy import deepcopy
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import networkx as nx
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from networkx.classes.graph import Graph
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from networkx.classes.coreviews import AdjacencyView
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from networkx.classes.reportviews import (
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OutEdgeView,
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InEdgeView,
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DiDegreeView,
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InDegreeView,
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OutDegreeView,
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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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class DiGraph(Graph):
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"""
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Base class for directed graphs.
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A DiGraph stores nodes and edges with optional data, or attributes.
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DiGraphs hold directed edges. Self loops are allowed but multiple
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(parallel) edges are not.
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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, NumPy matrix
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or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph.
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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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MultiGraph
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MultiDiGraph
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OrderedDiGraph
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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.DiGraph()
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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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>>> G.add_edge(1, 2)
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a list of edges,
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>>> G.add_edges_from([(1, 2), (1, 3)])
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or a collection of edges,
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>>> 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. There are no errors when adding
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nodes or edges that already exist.
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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.DiGraph(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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>>> G.add_edge(1, 2, weight=4.7)
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>>> G.add_edges_from([(3, 4), (4, 5)], color="red")
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>>> G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
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>>> G[1][2]["weight"] = 4.7
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>>> G.edges[1, 2]["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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Often the best way to traverse all edges of a graph is via the neighbors.
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The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`
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>>> for n, nbrsdict in G.adjacency():
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... for nbr, eattr in nbrsdict.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 reporting object is often more convenient:
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>>> for u, v, weight in G.edges(data="weight"):
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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 object-attributes and methods.
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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 Graph class uses a dict-of-dict-of-dict data 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 data keyed by neighbor. 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 three dicts can be replaced in a subclass by a user defined
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dict-like object. In general, the dict-like features should be
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maintained but extra features can be added. To replace one of the
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dicts create a new graph class by changing the class(!) variable
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holding the 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_attr_dict_factory 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, optional (default: dict)
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Factory function to be used to create the adjacency list
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dict which holds edge data keyed by neighbor.
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It should require no arguments and return a dict-like object
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edge_attr_dict_factory : function, optional (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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Examples
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--------
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Create a low memory graph class that effectively disallows edge
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attributes by using a single attribute dict for all edges.
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This reduces the memory used, but you lose edge attributes.
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>>> class ThinGraph(nx.Graph):
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... all_edge_dict = {"weight": 1}
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...
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... def single_edge_dict(self):
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... return self.all_edge_dict
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...
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... edge_attr_dict_factory = single_edge_dict
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>>> G = ThinGraph()
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>>> G.add_edge(2, 1)
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>>> G[2][1]
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{'weight': 1}
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>>> G.add_edge(2, 2)
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>>> G[2][1] is G[2][2]
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True
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Please see :mod:`~networkx.classes.ordered` for more 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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def __init__(self, incoming_graph_data=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 (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 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 NumPy matrix
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or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
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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.graph_attr_dict_factory = self.graph_attr_dict_factory
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self.node_dict_factory = self.node_dict_factory
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self.node_attr_dict_factory = self.node_attr_dict_factory
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self.adjlist_outer_dict_factory = self.adjlist_outer_dict_factory
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self.adjlist_inner_dict_factory = self.adjlist_inner_dict_factory
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self.edge_attr_dict_factory = self.edge_attr_dict_factory
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self.graph = self.graph_attr_dict_factory() # dictionary for graph attributes
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self._node = self.node_dict_factory() # dictionary for node attr
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# We store two adjacency lists:
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# the predecessors of node n are stored in the dict self._pred
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# the successors of node n are stored in the dict self._succ=self._adj
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self._adj = self.adjlist_outer_dict_factory() # empty adjacency dict
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self._pred = self.adjlist_outer_dict_factory() # predecessor
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self._succ = self._adj # successor
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# attempt to load graph with data
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if incoming_graph_data is not None:
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convert.to_networkx_graph(incoming_graph_data, create_using=self)
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# load graph attributes (must be after convert)
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self.graph.update(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 edge-data-dict. So `G.adj[3][2]['color'] = 'blue'` sets
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the color of the edge `(3, 2)` 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 AdjacencyView(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 edge-data-dict. So `G.succ[3][2]['color'] = 'blue'` sets
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the color of the edge `(3, 2)` to `"blue"`.
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Iterating over G.succ behaves like a dict. Useful idioms include
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`for nbr, datadict in G.succ[n].items():`. A data-view not provided
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by dicts also exists: `for nbr, foovalue in G.succ[node].data('foo'):`
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and a default can be set via a `default` argument to the `data` method.
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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` is identical to `G.succ`.
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"""
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return AdjacencyView(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 edge-data-dict. So `G.pred[2][3]['color'] = 'blue'` sets
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the color of the edge `(3, 2)` to `"blue"`.
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Iterating over G.pred behaves like a dict. Useful idioms include
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`for nbr, datadict in G.pred[n].items():`. A data-view not provided
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by dicts also exists: `for nbr, foovalue in G.pred[node].data('foo'):`
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A default can be set via a `default` argument to the `data` method.
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"""
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return AdjacencyView(self._pred)
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def add_node(self, node_for_adding, **attr):
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"""Add a single node `node_for_adding` and update node attributes.
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Parameters
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----------
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node_for_adding : node
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A node can be any hashable Python object except None.
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attr : keyword arguments, optional
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Set or change node attributes using key=value.
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See Also
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--------
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add_nodes_from
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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.add_node(1)
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>>> G.add_node("Hello")
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>>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
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>>> G.add_node(K3)
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>>> G.number_of_nodes()
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3
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Use keywords set/change node attributes:
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>>> G.add_node(1, size=10)
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>>> G.add_node(3, weight=0.4, UTM=("13S", 382871, 3972649))
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Notes
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-----
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A hashable object is one that can be used as a key in a Python
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dictionary. This includes strings, numbers, tuples of strings
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and numbers, etc.
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On many platforms hashable items also include mutables such as
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NetworkX Graphs, though one should be careful that the hash
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doesn't change on mutables.
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"""
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if node_for_adding not in self._succ:
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self._succ[node_for_adding] = self.adjlist_inner_dict_factory()
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self._pred[node_for_adding] = self.adjlist_inner_dict_factory()
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attr_dict = self._node[node_for_adding] = self.node_attr_dict_factory()
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attr_dict.update(attr)
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else: # update attr even if node already exists
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self._node[node_for_adding].update(attr)
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def add_nodes_from(self, nodes_for_adding, **attr):
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"""Add multiple nodes.
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Parameters
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----------
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nodes_for_adding : iterable container
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A container of nodes (list, dict, set, etc.).
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OR
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A container of (node, attribute dict) tuples.
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Node attributes are updated using the attribute dict.
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attr : keyword arguments, optional (default= no attributes)
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Update attributes for all nodes in nodes.
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Node attributes specified in nodes as a tuple take
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precedence over attributes specified via keyword arguments.
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See Also
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--------
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add_node
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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.add_nodes_from("Hello")
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>>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
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>>> G.add_nodes_from(K3)
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>>> sorted(G.nodes(), key=str)
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[0, 1, 2, 'H', 'e', 'l', 'o']
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Use keywords to update specific node attributes for every node.
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>>> G.add_nodes_from([1, 2], size=10)
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>>> G.add_nodes_from([3, 4], weight=0.4)
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Use (node, attrdict) tuples to update attributes for specific nodes.
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>>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})])
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>>> G.nodes[1]["size"]
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11
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>>> H = nx.Graph()
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>>> H.add_nodes_from(G.nodes(data=True))
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>>> H.nodes[1]["size"]
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11
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"""
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for n in nodes_for_adding:
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# keep all this inside try/except because
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# CPython throws TypeError on n not in self._succ,
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# while pre-2.7.5 ironpython throws on self._succ[n]
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try:
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if n not in self._succ:
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self._succ[n] = self.adjlist_inner_dict_factory()
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self._pred[n] = self.adjlist_inner_dict_factory()
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attr_dict = self._node[n] = self.node_attr_dict_factory()
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attr_dict.update(attr)
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else:
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self._node[n].update(attr)
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except TypeError:
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nn, ndict = n
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if nn not in self._succ:
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self._succ[nn] = self.adjlist_inner_dict_factory()
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self._pred[nn] = self.adjlist_inner_dict_factory()
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newdict = attr.copy()
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newdict.update(ndict)
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attr_dict = self._node[nn] = self.node_attr_dict_factory()
|
|
attr_dict.update(newdict)
|
|
else:
|
|
olddict = self._node[nn]
|
|
olddict.update(attr)
|
|
olddict.update(ndict)
|
|
|
|
def remove_node(self, n):
|
|
"""Remove node n.
|
|
|
|
Removes the node n and all adjacent edges.
|
|
Attempting to remove a non-existent node will raise an exception.
|
|
|
|
Parameters
|
|
----------
|
|
n : node
|
|
A node in the graph
|
|
|
|
Raises
|
|
-------
|
|
NetworkXError
|
|
If n is not in the graph.
|
|
|
|
See Also
|
|
--------
|
|
remove_nodes_from
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc
|
|
>>> list(G.edges)
|
|
[(0, 1), (1, 2)]
|
|
>>> G.remove_node(1)
|
|
>>> list(G.edges)
|
|
[]
|
|
|
|
"""
|
|
try:
|
|
nbrs = self._succ[n]
|
|
del self._node[n]
|
|
except KeyError as e: # NetworkXError if n not in self
|
|
raise NetworkXError(f"The node {n} is not in the digraph.") from e
|
|
for u in nbrs:
|
|
del self._pred[u][n] # remove all edges n-u in digraph
|
|
del self._succ[n] # remove node from succ
|
|
for u in self._pred[n]:
|
|
del self._succ[u][n] # remove all edges n-u in digraph
|
|
del self._pred[n] # remove node from pred
|
|
|
|
def remove_nodes_from(self, nodes):
|
|
"""Remove multiple nodes.
|
|
|
|
Parameters
|
|
----------
|
|
nodes : iterable container
|
|
A container of nodes (list, dict, set, etc.). If a node
|
|
in the container is not in the graph it is silently ignored.
|
|
|
|
See Also
|
|
--------
|
|
remove_node
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc
|
|
>>> e = list(G.nodes)
|
|
>>> e
|
|
[0, 1, 2]
|
|
>>> G.remove_nodes_from(e)
|
|
>>> list(G.nodes)
|
|
[]
|
|
|
|
"""
|
|
for n in nodes:
|
|
try:
|
|
succs = self._succ[n]
|
|
del self._node[n]
|
|
for u in succs:
|
|
del self._pred[u][n] # remove all edges n-u in digraph
|
|
del self._succ[n] # now remove node
|
|
for u in self._pred[n]:
|
|
del self._succ[u][n] # remove all edges n-u in digraph
|
|
del self._pred[n] # now remove node
|
|
except KeyError:
|
|
pass # silent failure on remove
|
|
|
|
def add_edge(self, u_of_edge, v_of_edge, **attr):
|
|
"""Add an edge between u and v.
|
|
|
|
The nodes u and v will be automatically added if they are
|
|
not already in 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) Python objects.
|
|
attr : keyword arguments, optional
|
|
Edge data (or labels or objects) can be assigned using
|
|
keyword arguments.
|
|
|
|
See Also
|
|
--------
|
|
add_edges_from : add a collection of edges
|
|
|
|
Notes
|
|
-----
|
|
Adding an edge that already exists updates the edge data.
|
|
|
|
Many NetworkX 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 = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
|
|
>>> e = (1, 2)
|
|
>>> G.add_edge(1, 2) # explicit two-node form
|
|
>>> G.add_edge(*e) # single edge as tuple of two nodes
|
|
>>> G.add_edges_from([(1, 2)]) # add edges from iterable container
|
|
|
|
Associate data to edges using keywords:
|
|
|
|
>>> G.add_edge(1, 2, weight=3)
|
|
>>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
|
|
|
|
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})
|
|
"""
|
|
u, v = u_of_edge, v_of_edge
|
|
# add nodes
|
|
if u not in self._succ:
|
|
self._succ[u] = self.adjlist_inner_dict_factory()
|
|
self._pred[u] = self.adjlist_inner_dict_factory()
|
|
self._node[u] = self.node_attr_dict_factory()
|
|
if v not in self._succ:
|
|
self._succ[v] = self.adjlist_inner_dict_factory()
|
|
self._pred[v] = self.adjlist_inner_dict_factory()
|
|
self._node[v] = self.node_attr_dict_factory()
|
|
# add the edge
|
|
datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
|
|
datadict.update(attr)
|
|
self._succ[u][v] = datadict
|
|
self._pred[v][u] = datadict
|
|
|
|
def add_edges_from(self, ebunch_to_add, **attr):
|
|
"""Add all the edges in ebunch_to_add.
|
|
|
|
Parameters
|
|
----------
|
|
ebunch_to_add : container of edges
|
|
Each edge given in the container will be added to the
|
|
graph. The edges must be given as 2-tuples (u, v) or
|
|
3-tuples (u, v, d) where d is a dictionary containing edge data.
|
|
attr : keyword arguments, optional
|
|
Edge data (or labels or objects) can be assigned using
|
|
keyword arguments.
|
|
|
|
See Also
|
|
--------
|
|
add_edge : add a single edge
|
|
add_weighted_edges_from : convenient way to add weighted edges
|
|
|
|
Notes
|
|
-----
|
|
Adding the same edge twice has no effect but any edge data
|
|
will be updated when each duplicate edge is added.
|
|
|
|
Edge attributes specified in an ebunch take precedence over
|
|
attributes specified via keyword arguments.
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
|
|
>>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples
|
|
>>> e = zip(range(0, 3), range(1, 4))
|
|
>>> G.add_edges_from(e) # Add the path graph 0-1-2-3
|
|
|
|
Associate data to edges
|
|
|
|
>>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
|
|
>>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
|
|
"""
|
|
for e in ebunch_to_add:
|
|
ne = len(e)
|
|
if ne == 3:
|
|
u, v, dd = e
|
|
elif ne == 2:
|
|
u, v = e
|
|
dd = {}
|
|
else:
|
|
raise NetworkXError(f"Edge tuple {e} must be a 2-tuple or 3-tuple.")
|
|
if u not in self._succ:
|
|
self._succ[u] = self.adjlist_inner_dict_factory()
|
|
self._pred[u] = self.adjlist_inner_dict_factory()
|
|
self._node[u] = self.node_attr_dict_factory()
|
|
if v not in self._succ:
|
|
self._succ[v] = self.adjlist_inner_dict_factory()
|
|
self._pred[v] = self.adjlist_inner_dict_factory()
|
|
self._node[v] = self.node_attr_dict_factory()
|
|
datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
|
|
datadict.update(attr)
|
|
datadict.update(dd)
|
|
self._succ[u][v] = datadict
|
|
self._pred[v][u] = datadict
|
|
|
|
def remove_edge(self, u, v):
|
|
"""Remove the edge between u and v.
|
|
|
|
Parameters
|
|
----------
|
|
u, v : nodes
|
|
Remove the edge between nodes u and v.
|
|
|
|
Raises
|
|
------
|
|
NetworkXError
|
|
If there is not an edge between u and v.
|
|
|
|
See Also
|
|
--------
|
|
remove_edges_from : remove a collection of edges
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.Graph() # or DiGraph, etc
|
|
>>> 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
|
|
>>> e = (2, 3, {"weight": 7}) # an edge with attribute data
|
|
>>> G.remove_edge(*e[:2]) # select first part of edge tuple
|
|
"""
|
|
try:
|
|
del self._succ[u][v]
|
|
del self._pred[v][u]
|
|
except KeyError as e:
|
|
raise NetworkXError(f"The edge {u}-{v} not in graph.") from e
|
|
|
|
def remove_edges_from(self, ebunch):
|
|
"""Remove all edges specified in ebunch.
|
|
|
|
Parameters
|
|
----------
|
|
ebunch: list or container of edge tuples
|
|
Each edge given in the list or container will be removed
|
|
from the graph. The edges can be:
|
|
|
|
- 2-tuples (u, v) edge between u and v.
|
|
- 3-tuples (u, v, k) where k is ignored.
|
|
|
|
See Also
|
|
--------
|
|
remove_edge : remove a single edge
|
|
|
|
Notes
|
|
-----
|
|
Will fail silently if an edge in ebunch is not in the graph.
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
|
|
>>> ebunch = [(1, 2), (2, 3)]
|
|
>>> G.remove_edges_from(ebunch)
|
|
"""
|
|
for e in ebunch:
|
|
u, v = e[:2] # ignore edge data
|
|
if u in self._succ and v in self._succ[u]:
|
|
del self._succ[u][v]
|
|
del self._pred[v][u]
|
|
|
|
def has_successor(self, u, v):
|
|
"""Returns True if node u has successor v.
|
|
|
|
This is true if graph has the edge u->v.
|
|
"""
|
|
return u in self._succ and v in self._succ[u]
|
|
|
|
def has_predecessor(self, u, v):
|
|
"""Returns True if node u has predecessor v.
|
|
|
|
This is true if graph has the edge u<-v.
|
|
"""
|
|
return u in self._pred and v in self._pred[u]
|
|
|
|
def successors(self, 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.
|
|
"""
|
|
try:
|
|
return iter(self._succ[n])
|
|
except KeyError as e:
|
|
raise NetworkXError(f"The node {n} is not in the digraph.") from e
|
|
|
|
# digraph definitions
|
|
neighbors = successors
|
|
|
|
def predecessors(self, n):
|
|
"""Returns an iterator over predecessor nodes of n.
|
|
|
|
A predecessor of n is a node m such that there exists a directed
|
|
edge from m to n.
|
|
|
|
Parameters
|
|
----------
|
|
n : node
|
|
A node in the graph
|
|
|
|
Raises
|
|
-------
|
|
NetworkXError
|
|
If n is not in the graph.
|
|
|
|
See Also
|
|
--------
|
|
successors
|
|
"""
|
|
try:
|
|
return iter(self._pred[n])
|
|
except KeyError as e:
|
|
raise NetworkXError(f"The node {n} is not in the digraph.") from e
|
|
|
|
@property
|
|
def edges(self):
|
|
"""An OutEdgeView of the DiGraph as G.edges or 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)])
|
|
|
|
"""
|
|
return OutEdgeView(self)
|
|
|
|
# alias out_edges to edges
|
|
out_edges = edges
|
|
|
|
@property
|
|
def in_edges(self):
|
|
"""An InEdgeView of the Graph as G.in_edges or G.in_edges().
|
|
|
|
in_edges(self, nbunch=None, data=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).
|
|
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 : InEdgeView
|
|
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
|
|
--------
|
|
edges
|
|
"""
|
|
return InEdgeView(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 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, degree).
|
|
|
|
See Also
|
|
--------
|
|
in_degree, out_degree
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.DiGraph() # or 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 DiDegreeView(self)
|
|
|
|
@property
|
|
def in_degree(self):
|
|
"""An InDegreeView for (node, in_degree) or in_degree for single node.
|
|
|
|
The node in_degree is the number of edges pointing to the node.
|
|
The weighted node degree is the sum of the edge weights for
|
|
edges incident to that node.
|
|
|
|
This object provides an iteration over (node, in_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 node is requested
|
|
deg : int
|
|
In-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.DiGraph()
|
|
>>> 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 InDegreeView(self)
|
|
|
|
@property
|
|
def out_degree(self):
|
|
"""An OutDegreeView for (node, out_degree)
|
|
|
|
The node out_degree is the number of edges pointing out of the node.
|
|
The weighted node degree is the sum of the edge weights for
|
|
edges incident to that node.
|
|
|
|
This object provides an iterator over (node, out_degree) as well as
|
|
lookup for the degree for a single node.
|
|
|
|
Parameters
|
|
----------
|
|
nbunch : single node, container, or all nodes (default= all nodes)
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|
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 node is requested
|
|
deg : int
|
|
Out-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.DiGraph()
|
|
>>> 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 OutDegreeView(self)
|
|
|
|
def clear(self):
|
|
"""Remove all nodes and edges from the graph.
|
|
|
|
This also removes the name, and all graph, node, and edge attributes.
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
|
|
>>> G.clear()
|
|
>>> list(G.nodes)
|
|
[]
|
|
>>> list(G.edges)
|
|
[]
|
|
|
|
"""
|
|
self._succ.clear()
|
|
self._pred.clear()
|
|
self._node.clear()
|
|
self.graph.clear()
|
|
|
|
def clear_edges(self):
|
|
"""Remove all edges from the graph without altering nodes.
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
|
|
>>> G.clear_edges()
|
|
>>> list(G.nodes)
|
|
[0, 1, 2, 3]
|
|
>>> list(G.edges)
|
|
[]
|
|
|
|
"""
|
|
for predecessor_dict in self._pred.values():
|
|
predecessor_dict.clear()
|
|
for successor_dict in self._succ.values():
|
|
successor_dict.clear()
|
|
|
|
def is_multigraph(self):
|
|
"""Returns True if graph is a multigraph, False otherwise."""
|
|
return False
|
|
|
|
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 : Graph
|
|
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
|
|
--------
|
|
Graph, copy, add_edge, add_edges_from
|
|
|
|
Notes
|
|
-----
|
|
If edges in both directions (u, v) and (v, u) exist in the
|
|
graph, attributes for the new undirected edge will be a combination of
|
|
the attributes of the directed edges. The edge data is updated
|
|
in the (arbitrary) order that the edges are encountered. For
|
|
more customized control of the edge attributes use add_edge().
|
|
|
|
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 G=DiGraph(D) 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 DiGraph to use dict-like objects
|
|
in the data structure, those changes do not transfer to the
|
|
Graph 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)
|
|
# deepcopy when not a view
|
|
G = Graph()
|
|
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, deepcopy(d))
|
|
for u, nbrs in self._adj.items()
|
|
for v, d in nbrs.items()
|
|
if v in self._pred[u]
|
|
)
|
|
else:
|
|
G.add_edges_from(
|
|
(u, v, deepcopy(d))
|
|
for u, nbrs in self._adj.items()
|
|
for v, d in nbrs.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.nodes.items())
|
|
H.add_edges_from((v, u, deepcopy(d)) for u, v, d in self.edges(data=True))
|
|
return H
|
|
return nx.graphviews.reverse_view(self)
|
|
|