This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

Source code for networkx.relabel

#    Copyright (C) 2006-2013 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.
import networkx as nx
__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>',
                           'Pieter Swart (swart@lanl.gov)',
                           'Dan Schult (dschult@colgate.edu)'])
__all__ = ['convert_node_labels_to_integers', 'relabel_nodes']

[docs]def relabel_nodes(G, mapping, copy=True): """Relabel the nodes of the graph G. Parameters ---------- G : graph A NetworkX graph mapping : dictionary A dictionary with the old labels as keys and new labels as values. A partial mapping is allowed. copy : bool (optional, default=True) If True return a copy, or if False relabel the nodes in place. Examples -------- >>> G=nx.path_graph(3) # nodes 0-1-2 >>> mapping={0:'a',1:'b',2:'c'} >>> H=nx.relabel_nodes(G,mapping) >>> print(sorted(H.nodes())) ['a', 'b', 'c'] >>> G=nx.path_graph(26) # nodes 0..25 >>> mapping=dict(zip(G.nodes(),"abcdefghijklmnopqrstuvwxyz")) >>> H=nx.relabel_nodes(G,mapping) # nodes a..z >>> mapping=dict(zip(G.nodes(),range(1,27))) >>> G1=nx.relabel_nodes(G,mapping) # nodes 1..26 Partial in-place mapping: >>> G=nx.path_graph(3) # nodes 0-1-2 >>> mapping={0:'a',1:'b'} # 0->'a' and 1->'b' >>> G=nx.relabel_nodes(G,mapping, copy=False) print(G.nodes()) [2, 'b', 'a'] Mapping as function: >>> G=nx.path_graph(3) >>> def mapping(x): ... return x**2 >>> H=nx.relabel_nodes(G,mapping) >>> print(H.nodes()) [0, 1, 4] Notes ----- Only the nodes specified in the mapping will be relabeled. The keyword setting copy=False modifies the graph in place. This is not always possible if the mapping is circular. In that case use copy=True. See Also -------- convert_node_labels_to_integers """ # you can pass a function f(old_label)->new_label # but we'll just make a dictionary here regardless if not hasattr(mapping,"__getitem__"): m = dict((n, mapping(n)) for n in G) else: m = mapping if copy: return _relabel_copy(G, m) else: return _relabel_inplace(G, m)
def _relabel_inplace(G, mapping): old_labels = set(mapping.keys()) new_labels = set(mapping.values()) if len(old_labels & new_labels) > 0: # labels sets overlap # can we topological sort and still do the relabeling? D = nx.DiGraph(list(mapping.items())) D.remove_edges_from(D.selfloop_edges()) try: nodes = nx.topological_sort(D, reverse=True) except nx.NetworkXUnfeasible: raise nx.NetworkXUnfeasible('The node label sets are overlapping ' 'and no ordering can resolve the ' 'mapping. Use copy=True.') else: # non-overlapping label sets nodes = old_labels multigraph = G.is_multigraph() directed = G.is_directed() for old in nodes: try: new = mapping[old] except KeyError: continue try: G.add_node(new, attr_dict=G.node[old]) except KeyError: raise KeyError("Node %s is not in the graph"%old) if multigraph: new_edges = [(new, old == target and new or target, key, data) for (_,target,key,data) in G.edges(old, data=True, keys=True)] if directed: new_edges += [(old == source and new or source, new, key, data) for (source, _, key,data) in G.in_edges(old, data=True, keys=True)] else: new_edges = [(new, old == target and new or target, data) for (_,target,data) in G.edges(old, data=True)] if directed: new_edges += [(old == source and new or source,new,data) for (source,_,data) in G.in_edges(old, data=True)] G.remove_node(old) G.add_edges_from(new_edges) return G def _relabel_copy(G, mapping): H = G.__class__() H.name = "(%s)" % G.name if G.is_multigraph(): H.add_edges_from( (mapping.get(n1, n1),mapping.get(n2, n2),k,d.copy()) for (n1,n2,k,d) in G.edges_iter(keys=True, data=True)) else: H.add_edges_from( (mapping.get(n1, n1),mapping.get(n2, n2),d.copy()) for (n1, n2, d) in G.edges_iter(data=True)) H.add_nodes_from(mapping.get(n, n) for n in G) H.node.update(dict((mapping.get(n, n), d.copy()) for n,d in G.node.items())) H.graph.update(G.graph.copy()) return H
[docs]def convert_node_labels_to_integers(G, first_label=0, ordering="default", label_attribute=None): """Return a copy of the graph G with the nodes relabeled using consecutive integers. Parameters ---------- G : graph A NetworkX graph first_label : int, optional (default=0) An integer specifying the starting offset in numbering nodes. The new integer labels are numbered first_label, ..., n-1+first_label. ordering : string "default" : inherit node ordering from G.nodes() "sorted" : inherit node ordering from sorted(G.nodes()) "increasing degree" : nodes are sorted by increasing degree "decreasing degree" : nodes are sorted by decreasing degree label_attribute : string, optional (default=None) Name of node attribute to store old label. If None no attribute is created. Notes ----- Node and edge attribute data are copied to the new (relabeled) graph. See Also -------- relabel_nodes """ N = G.number_of_nodes()+first_label if ordering == "default": mapping = dict(zip(G.nodes(), range(first_label, N))) elif ordering == "sorted": nlist = G.nodes() nlist.sort() mapping = dict(zip(nlist, range(first_label, N))) elif ordering == "increasing degree": dv_pairs = [(d,n) for (n,d) in G.degree_iter()] dv_pairs.sort() # in-place sort from lowest to highest degree mapping = dict(zip([n for d,n in dv_pairs], range(first_label, N))) elif ordering == "decreasing degree": dv_pairs = [(d,n) for (n,d) in G.degree_iter()] dv_pairs.sort() # in-place sort from lowest to highest degree dv_pairs.reverse() mapping = dict(zip([n for d,n in dv_pairs], range(first_label, N))) else: raise nx.NetworkXError('Unknown node ordering: %s'%ordering) H = relabel_nodes(G, mapping) H.name = "("+G.name+")_with_int_labels" # create node attribute with the old label if label_attribute is not None: nx.set_node_attributes(H, label_attribute, dict((v,k) for k,v in mapping.items())) return H