""" Base class for digraphs. """ __author__ = """Aric Hagberg (hagberg@lanl.gov)\nPieter Swart (swart@lanl.gov)\nDan Schult(dschult@colgate.edu)""" # Copyright (C) 2004-2006 by # Aric Hagberg # Dan Schult # Pieter Swart # Distributed under the terms of the GNU Lesser General Public License # http://www.gnu.org/copyleft/lesser.html # from graph import Graph from networkx.exception import NetworkXException, NetworkXError import networkx.convert as convert class DiGraph(Graph): """ A graph with directed edges. Subclass of Graph. DiGraph inherits from Graph, overriding the following methods: - __init__: replaces self.adj with the dicts self.pred and self.succ - add_node - delete_node - add_edge - delete_edge - add_nodes_from - delete_nodes_from - add_edges_from - delete_edges_from - edges_iter - degree_iter - copy - clear - subgraph - is_directed - to_directed - to_undirected Digraph adds the following methods to those of Graph: - successors - successors_iter - predecessors - predecessors_iter - out_degree - out_degree_iter - in_degree - in_degree_iter """ # we store two adjacency lists: # the predecessors of node n are stored in the dict self.pred # the successors of node n are stored in the dict self.succ=self.adj def __init__(self,data=None,name=None,**kwds): self.name='' self.adj={} # empty adjacency hash self.pred={} # predecessor self.succ=self.adj # successor if data is not None: self=convert.from_whatever(data,create_using=self) if name is not None: self.name=name def add_node(self, n): """Add a single node to the digraph. The node n can be any hashable object except None. A hashable object is one that can be used as a key in a Python dictionary. This includes strings, numbers, tuples of strings and numbers, etc. On many platforms this also includes mutables such as Graphs, though one should be careful that the hash doesn't change on mutables. >>> from networkx import * >>> G=DiGraph() >>> K3=complete_graph(3) >>> G.add_nodes_from(K3) # add the nodes from K3 to G >>> G.nodes() [0, 1, 2] >>> G.clear() >>> G.add_node(K3) # add the graph K3 as a node in G. >>> G.number_of_nodes() 1 """ if n not in self.succ: self.succ[n]={} if n not in self.pred: self.pred[n]={} def add_nodes_from(self, nbunch): """Add multiple nodes to the digraph. nbunch: A container of nodes that will be iterated through once (thus it should be an iterator or be iterable). Each element of the container should be a valid node type: any hashable type except None. See add_node for details. """ for n in nbunch: if n not in self.succ: self.succ[n]={} if n not in self.pred: self.pred[n]={} def delete_node(self,n): """Delete node n from the digraph. Attempting to delete a non-existent node will raise a NetworkXError. """ try: for u in self.succ[n].keys(): del self.pred[u][n] # remove all edges n-u in graph del self.succ[n] # remove node from succ for u in self.pred[n].keys(): del self.succ[u][n] # remove all edges n-u in graph del self.pred[n] # remove node from pred except KeyError: # NetworkXError if n not in self raise NetworkXError, "node %s not in graph"%n def delete_nodes_from(self,nbunch): """Remove nodes in nbunch from the digraph. nbunch: an iterable or iterator containing valid node names. Attempting to delete a non-existent node will raise an exception. This could mean some nodes in nbunch were deleted and some valid nodes were not! """ for n in nbunch: try: for u in self.succ[n].keys(): # self.delete_edge(n,u)# remove all edges n-u in graph del self.pred[u][n] # remove all edges n-u in graph del self.succ[n] # now remove node for u in self.pred[n].keys(): # self.delete_edge(u,n)# remove all edges u-n in graph del self.succ[u][n] # remove all edges n-u in graph del self.pred[n] # now remove node except KeyError: # NetworkXError if n not in self raise NetworkXError, "node %s not in graph"%n def add_edge(self, u, v=None): """Add a single directed edge (u,v) to the digraph. >> G.add_edge(u,v) and >>> G.add_edge( (u,v) ) are equivalent forms of adding a single edge between nodes u and v. The nodes u and v will be automatically added if not already in the graph. They must be a hashable (except None) Python object. For example, the following examples all add the edge (1,2) to the digraph G. >>> G=DiGraph() >>> G.add_edge( 1, 2 ) # explicit two node form >>> G.add_edge( (1,2) ) # single edge as tuple of two nodes >>> G.add_edges_from( [(1,2)] ) # list of edges form """ if v is None: (u,v)=u # no v given, assume u is an edge tuple # add nodes if u not in self.succ: self.succ[u]={} if u not in self.pred: self.pred[u]={} if v not in self.succ: self.succ[v]={} if v not in self.pred: self.pred[v]={} # don't create self loops, fail silently, nodes are still added if u==v: return self.succ[u][v]=None self.pred[v][u]=None def add_edges_from(self, ebunch): """Add all the edges in ebunch to the graph. ebunch: Container of 2-tuples (u,v). The container must be iterable or an iterator. It is iterated over once. Adding the same edge twice has no effect and does not raise an exception. See add_edge for an example. """ for e in ebunch: (u,v)=e # add nodes if u not in self.succ: self.succ[u]={} if u not in self.pred: self.pred[u]={} if v not in self.succ: self.succ[v]={} if v not in self.pred: self.pred[v]={} # don't create self loops, fail silently, nodes are still added if u==v: continue self.succ[u][v]=None self.pred[v][u]=None def delete_edge(self, u, v=None): """Delete the single directed edge (u,v) from the digraph. Can be used in two basic forms >>> G.delete_edge(u,v) and G.delete_edge( (u,v) ) are equivalent ways of deleting a directed edge u->v. If the edge does not exist return without complaining. """ if v is None: (u,v)=u if u in self.pred[v] and v in self.succ[u]: del self.succ[u][v] del self.pred[v][u] def delete_edges_from(self, ebunch): """Delete the directed edges in ebunch from the digraph. ebunch: Container of 2-tuples (u,v). The container must be iterable or an iterator. It is iterated over once. Edges that are not in the digraph are ignored. """ for (u,v) in ebunch: if u in self.pred[v] and v in self.succ[u]: del self.succ[u][v] del self.pred[v][u] def out_edges_iter(self, nbunch=None): """Return iterator that iterates once over each edge pointing out of nodes in nbunch, or over all edges in digraph if no nodes are specified. See add_node for definition of nbunch. Nodes in nbunch that are not in the graph will be (quietly) ignored. """ # prepare nbunch if nbunch is None: # include all nodes via iterator bunch=self.nodes_iter() elif nbunch in self: # if nbunch is a single node bunch=[nbunch] else: # if nbunch is a sequence of nodes try: bunch=[n for n in nbunch if n in self] except TypeError: raise NetworkXError, "nbunch is not a node or a sequence of nodes." # nbunch ready for n in bunch: for u in self.succ[n]: yield (n,u) def in_edges_iter(self, nbunch=None): """Return iterator that iterates once over each edge adjacent to nodes in nbunch, or over all edges in digraph if no nodes are specified. See add_node for definition of nbunch. Nodes in nbunch that are not in the graph will be (quietly) ignored. """ # prepare nbunch if nbunch is None: # include all nodes via iterator bunch=self.nodes_iter() elif nbunch in self: # if nbunch is a single node bunch=[nbunch] else: # if nbunch is a sequence of nodes try: bunch=[n for n in nbunch if n in self] except TypeError: raise NetworkXError, "nbunch is not a node or a sequence of nodes." # nbunch ready for n in bunch: for u in self.pred[n]: yield (u,n) # define edges to be out_edges implicitly since edges uses edges_iter edges_iter=out_edges_iter def out_edges(self, nbunch=None): """Return list of all edges that point out of nodes in nbunch, or a list of all edges in graph if no nodes are specified. See add_node for definition of nbunch. Those nodes in nbunch that are not in the graph will be (quietly) ignored. """ return list(self.out_edges_iter(nbunch)) def in_edges(self, nbunch=None): """Return list of all edges that point in to nodes in nbunch, or a list of all edges in graph if no nodes are specified. See add_node for definition of nbunch. Those nodes in nbunch that are not in the graph will be (quietly) ignored. """ return list(self.in_edges_iter(nbunch)) def successors_iter(self,v): """Return an iterator for successor nodes of v.""" try: return self.succ[v].iterkeys() except KeyError: raise NetworkXError, "node %s not in graph"%v def predecessors_iter(self,v): """Return an iterator for predecessor nodes of v.""" try: return self.pred[v].iterkeys() except KeyError: raise NetworkXError, "node %s not in graph"%v def successors(self, v): """Return sucessor nodes of v.""" return list(self.successors_iter(v)) def predecessors(self, v): """Return predecessor nodes of v.""" return list(self.predecessors_iter(v)) # digraph definintions out_neighbors=successors in_neighbors=predecessors neighbors=successors neighbors_iter=successors_iter def degree_iter(self,nbunch=None,with_labels=False): """Return iterator that returns in_degree(n)+out_degree(n) or (n,in_degree(n)+out_degree(n)) for all n in nbunch. If nbunch is ommitted, then iterate over *all* nodes. Can be called in three ways: G.degree_iter(n): return iterator the degree of node n G.degree_iter(nbunch): return a list of values, one for each n in nbunch (nbunch is any iterable container of nodes.) G.degree_iter(): same as nbunch = all nodes in graph. If with_labels=True, iterator will return an (n,in_degree(n)+out_degree(n)) tuple of node and degree. Any nodes in nbunch but not in the graph will be (quietly) ignored. """ # prepare nbunch if nbunch is None: # include all nodes via iterator bunch=self.nodes_iter() elif nbunch in self: # if nbunch is a single node bunch=[nbunch] else: # if nbunch is a sequence of nodes try: bunch=[n for n in nbunch if n in self] except TypeError: raise NetworkXError, "nbunch is not a node or a sequence of nodes." # nbunch ready if with_labels: # yield tuple (n,degree) for n in bunch: yield (n,len(self.succ[n])+len(self.pred[n])) else: for n in bunch: yield len(self.succ[n])+len(self.pred[n]) def in_degree_iter(self,nbunch=None,with_labels=False): """Return iterator for in_degree(n) or (n,in_degree(n)) for all n in nbunch. If nbunch is ommitted, then iterate over *all* nodes. See degree_iter method for Digraph Class for more details. """ # prepare nbunch if nbunch is None: # include all nodes via iterator bunch=self.nodes_iter() elif nbunch in self: # if nbunch is a single node bunch=[nbunch] else: # if nbunch is a sequence of nodes try: bunch=[n for n in nbunch if n in self] except TypeError: raise NetworkXError, "nbunch is not a node or a sequence of nodes." # nbunch ready if with_labels: # yield tuple (n,degree) for n in bunch: yield (n,len(self.pred[n])) else: for n in bunch: yield len(self.pred[n]) def out_degree_iter(self,nbunch=None,with_labels=False): """Return iterator for out_degree(n) or (n,out_degree(n)) for all n in nbunch. If nbunch is ommitted, then iterate over *all* nodes. See degree_iter method for Digraph Class for more details. """ # prepare nbunch if nbunch is None: # include all nodes via iterator bunch=self.nodes_iter() elif nbunch in self: # if nbunch is a single node bunch=[nbunch] else: # if nbunch is a sequence of nodes try: bunch=[n for n in nbunch if n in self] except TypeError: raise NetworkXError, "nbunch is not a node or a sequence of nodes." # nbunch ready if with_labels: # yield tuple (n,degree) for n in bunch: yield (n,len(self.succ[n])) else: for n in bunch: yield len(self.succ[n]) def out_degree(self, nbunch=None, with_labels=False): """Return out-degree of single node or of nbunch of nodes. If nbunch is omitted or nbunch=None, then return out-degrees of *all* nodes. """ if with_labels: # return a dict return dict(self.out_degree_iter(nbunch,with_labels)) elif nbunch in self: # return a single node return self.out_degree_iter(nbunch,with_labels).next() else: # return a list return list(self.out_degree_iter(nbunch,with_labels)) def in_degree(self, nbunch=None, with_labels=False): """Return in-degree of single node or of nbunch of nodes. If nbunch is omitted or nbunch=None, then return in-degrees of *all* nodes. """ if with_labels: # return a dict return dict(self.in_degree_iter(nbunch,with_labels)) elif nbunch in self: # return a single node return self.in_degree_iter(nbunch,with_labels).next() else: # return a list return list(self.in_degree_iter(nbunch,with_labels)) def clear(self): """Remove name and delete all nodes and edges from digraph.""" self.name=None self.succ.clear() self.pred.clear() def copy(self): """Return a (shallow) copy of the digraph. Identical to dict.copy() of adjacency dicts pred and succ, with name copied as well. """ H=self.__class__() H.name=self.name H.adj=self.adj.copy() H.succ=H.adj # fix pointer again for v in H.adj: H.pred[v]=self.pred[v].copy() H.succ[v]=self.succ[v].copy() return H def subgraph(self, nbunch, inplace=False, create_using=None): """ Return the subgraph induced on nodes in nbunch. nbunch: can be a singleton node, a string (which is treated as a singleton node), or any iterable container of of nodes. (It can be an iterable or an iterator, e.g. a list, set, graph, file, etc.) Setting inplace=True will return the induced subgraph in original graph by deleting nodes not in nbunch. This overrides create_using. Warning: this can destroy the graph. Unless otherwise specified, return a new graph of the same type as self. Use (optional) create_using=R to return the resulting subgraph in R. R can be an existing graph-like object (to be emptied) or R can be a call to a graph object, e.g. create_using=DiGraph(). See documentation for empty_graph() Note: use subgraph(G) rather than G.subgraph() to access the more general subgraph() function from the operators module. """ bunch=self.prepare_nbunch(nbunch) if inplace: # demolish all nodes (and attached edges) not in nbunch # override any setting of create_using bunch=dict.fromkeys(bunch) # make a dict self.delete_nodes_from([n for n in self if n not in bunch]) self.name="Subgraph of (%s)"%(self.name) return self # create new graph if create_using is None: # Graph object of the same type as current graph H=self.__class__() else: # user specified graph H=create_using H.clear() H.name="Subgraph of (%s)"%(self.name) H.add_nodes_from(bunch) # add edges H_succ=H.succ # cache H_pred=H.pred self_succ=self.succ self_pred=self.pred dict_fromkeys=dict.fromkeys for n in H: H_succ[n]=dict_fromkeys([u for u in self_succ[n] if u in H_succ], None) H_pred[n]=dict_fromkeys([u for u in self_pred[n] if u in H_succ], None) return H def is_directed(self): """ Return True if a directed graph.""" return True def to_undirected(self): """Return the undirected representation of the digraph. A new graph is returned (the underlying graph). The edge u-v is in the underlying graph if either u->v or v->u is in the digraph. """ H=Graph() H.name=self.name H.adj=self.succ.copy() # copy nodes for u in H.adj: H.adj[u]=self.succ[u].copy() # copy successors for v in self.pred[u]: # now add predecessors to adj too H.adj[u][v]=None return H def to_directed(self): """Return a directed representation of the digraph. This is already directed, so merely return a copy. """ return self.copy() def reverse(self): """ Return a new digraph with the same vertices and edges as G but with the directions of the edges reversed. """ H=self.__class__() # new empty DiGraph H.name="Reverse of (%s)"%(self.name) H.add_nodes_from(self) H.add_edges_from([(v,u) for (u,v) in self.edges_iter()]) return H def _test_suite(): import doctest suite = doctest.DocFileSuite('tests/digraph_DiGraph.txt', package='networkx') return suite if __name__ == "__main__": import os import sys import unittest if sys.version_info[:2] < (2, 4): print "Python version 2.4 or later required for tests (%d.%d detected)." % sys.version_info[:2] sys.exit(-1) # directory of networkx package (relative to this) nxbase=sys.path[0]+os.sep+os.pardir sys.path.insert(0,nxbase) # prepend to search path unittest.TextTestRunner().run(_test_suite())