File: relabel.py

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#    Copyright (C) 2006-2011 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 (hagberg@lanl.gov)',
                           'Pieter Swart (swart@lanl.gov)',
                           'Dan Schult(dschult@colgate.edu)'])
__all__ = ['convert_node_labels_to_integers', 'relabel_nodes'] 

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(H.nodes())
    ['a', 'c', 'b']

    >>> 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)
        except nx.NetworkXUnfeasible:
            raise nx.NetworkXUnfeasible('The node label sets are overlapping '
                                        'and no ordering can resolve the '
                                        'mapping. Use copy=True.')
        nodes.reverse()  # reverse topological order
    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        
    

def convert_node_labels_to_integers(G, first_label=0, ordering="default",
                                    discard_old_labels=True):
    """Return a copy of G node labels replaced with integers.

    Parameters
    ----------
    G : graph
       A NetworkX graph 

    first_label : int, optional (default=0)       
       An integer specifying the offset in numbering nodes.
       The n 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

    discard_old_labels : bool, optional (default=True)
       If True discard old labels. If False, create a node attribute 
       'old_label' to hold the old labels.
    """
#    This function strips information attached to the nodes and/or
#    edges of a graph, and returns a graph with appropriate integer
#    labels. One can view this as a re-labeling of the nodes. Be
#    warned that the term "labeled graph" has a loaded meaning
#    in graph theory. The fundamental issue is whether the names
#    (labels) of the nodes (and edges) matter in deciding when two
#    graphs are the same. For example, in problems of graph enumeration
#    there is a distinct difference in techniques required when
#    counting labeled vs. unlabeled graphs.

#    When implementing graph
#    algorithms it is often convenient to strip off the original node
#    and edge information and appropriately relabel the n nodes with
#    the integer values 1,..,n. This is the purpose of this function,
#    and it provides the option (see discard_old_labels variable) to either
#    preserve the original labels in separate dicts (these are not
#    returned but made an attribute of the new graph.

    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"
    if not discard_old_labels:
        H.node_labels=mapping
    return H