def predecessors_iter(self, n, t=None):
        """Return an iterator over predecessors nodes of n at time t (optional).

        Parameters
        ----------
        n : node
            Nodes can be, for example, strings or numbers.
            Nodes must be hashable (and not None) Python objects.
        t : snapshot id (default=None)
            If None will be returned the presence of the interaction on the flattened graph.


        """
        try:
            if t is None:
                return iter(self._pred[n])
            else:
                return iter([i for i in self._pred[n] if self.__presence_test(i, n, t)])
        except KeyError:
            raise nx.NetworkXError("The node %s is not in the graph." % (n,))
def predecessors_iter ( self , n , t = None ) : try : if t is None : return iter ( self . _pred [ n ] ) else : return iter ( [ i for i in self . _pred [ n ] if self . __presence_test ( i , n , t ) ] ) except KeyError : raise nx . NetworkXError ( "The node %s is not in the graph." % ( n , ) )
Return an iterator over predecessors nodes of n at time t (optional).

        Parameters
        ----------
        n : node
            Nodes can be, for example, strings or numbers.
            Nodes must be hashable (and not None) Python objects.
        t : snapshot id (default=None)
            If None will be returned the presence of the interaction on the flattened graph.