"""
NetworkX
========

   NetworkX (NX) is a Python package for the creation, manipulation, and
   study of the structure, dynamics, and functions of complex networks.  

   https://networkx.lanl.gov/

Using 
-----

   Just write in Python

   >>> import networkx as NX
   >>> G=NX.Graph()
   >>> G.add_edge(1,2)
   >>> G.add_node("spam")
   >>> print G.nodes()
   [1, 2, 'spam']
   >>> print G.edges()
   [(1, 2)]
"""
#    Copyright (C) 2004-2006 by 
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    Distributed under the terms of the GNU Lesser General Public License
#    http://www.gnu.org/copyleft/lesser.html
#
# Add platform dependent shared library path to sys.path
#
import sys
if sys.version_info[:2] < (2, 3):
    print "Python version 2.3 or later is required for NetworkX (%d.%d detected)." %  sys.version_info[:2]
    sys.exit(-1)
del sys
# Release data
import release 
__version__  = release.version
__date__     = release.date
__author__   = '%s <%s>\n%s <%s>\n%s <%s>' % \
              ( release.authors['Hagberg'] + release.authors['Schult'] + \
                release.authors['Swart'] )
__license__  = release.license

import info
__doc__+=info.__doc__

#
# NetworkX package modules
#
from exception import  NetworkXException, NetworkXError
from graph import Graph
from digraph import DiGraph
from function import  nodes, edges, degree, degree_histogram, neighbors,\
                 number_of_nodes, number_of_edges, density,\
                 nodes_iter, edges_iter, is_directed
from xgraph import XGraph
from xdigraph import XDiGraph
from distance import eccentricity, diameter, radius, periphery, center
from path import  \
     shortest_path, shortest_path_length,bidirectional_shortest_path,\
     single_source_shortest_path, single_source_shortest_path_length,\
     all_pairs_shortest_path, all_pairs_shortest_path_length,\
     dijkstra_path, dijkstra_path_length, bidirectional_dijkstra,\
     single_source_dijkstra_path, single_source_dijkstra_path_length,\
     single_source_dijkstra,\
     dijkstra_predecessor_and_distance, predecessor, floyd_warshall
from dag import \
     topological_sort, topological_sort_recursive,\
     is_directed_acyclic_graph
from component import \
     number_connected_components, connected_components,\
     is_connected, connected_component_subgraphs,\
     node_connected_component,\
     number_strongly_connected_components, strongly_connected_components,\
     is_strongly_connected, strongly_connected_component_subgraphs,\
     strongly_connected_components_recursive,\
     kosaraju_strongly_connected_components

from search import dfs_preorder, dfs_postorder, dfs_predecessor,\
            dfs_successor, dfs_tree
from cluster import triangles, average_clustering, clustering, transitivity
from operators import union, cartesian_product, compose, complement,\
                      disjoint_union, create_empty_copy,\
                      subgraph, convert_to_undirected, convert_to_directed,\
                      convert_node_labels_to_integers, relabel_nodes
from centrality import betweenness_centrality, \
                       betweenness_centrality_source, \
                       load_centrality,\
                       newman_betweenness_centrality,\
                       brandes_betweenness_centrality,\
                       edge_betweenness,\
                       edge_load,\
                       degree_centrality, \
                       closeness_centrality
from hybrid import kl_connected_subgraph, is_kl_connected
from cliques import find_cliques, make_max_clique_graph,\
                    make_clique_bipartite,graph_clique_number,\
                    graph_number_of_cliques,node_clique_number,\
                    number_of_cliques,cliques_containing_node
from isomorph import is_isomorphic
                    
# need numpy for spectrum
try:
    from spectrum import \
         adj_matrix, laplacian, generalized_laplacian,\
         laplacian_spectrum, adjacency_spectrum
except ImportError:
    pass

from utils import is_string_like, iterable,\
                  pareto_sequence, powerlaw_sequence, uniform_sequence,\
                  cumulative_distribution, discrete_sequence,_get_fh


from convert import from_whatever,\
     from_dict_of_dicts, to_dict_of_dicts,\
     from_dict_of_lists, to_dict_of_lists,\
     from_numpy_matrix, to_numpy_matrix,\
     from_scipy_sparse_matrix, to_scipy_sparse_matrix

# import some useful graphs - we always use these...
from generators.classic import *
from generators.small import *
from generators.random_graphs import *
from generators.degree_seq import *
from generators.directed import *
# drawing
from drawing import *
# read/write graphs in various formats
from readwrite import *

from tests import run as test