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#!/usr/bin/env python
from nose.tools import *
from nose import SkipTest
import networkx
class TestFlowClosenessCentrality(object):
numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
@classmethod
def setupClass(cls):
global np
try:
import numpy as np
import scipy
except ImportError:
raise SkipTest('NumPy not available.')
def test_K4(self):
"""Closeness centrality: K4"""
G=networkx.complete_graph(4)
b=networkx.current_flow_closeness_centrality(G,normalized=True)
b_answer={0: 2.0, 1: 2.0, 2: 2.0, 3: 2.0}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_P4_normalized(self):
"""Closeness centrality: P4 normalized"""
G=networkx.path_graph(4)
b=networkx.current_flow_closeness_centrality(G,normalized=True)
b_answer={0: 1./2, 1: 3./4, 2: 3./4, 3:1./2}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_P4(self):
"""Closeness centrality: P4"""
G=networkx.path_graph(4)
b=networkx.current_flow_closeness_centrality(G,normalized=False)
b_answer={0: 1.0/6, 1: 1.0/4, 2: 1.0/4, 3:1.0/6}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_star(self):
"""Closeness centrality: star """
G=networkx.Graph()
G.add_star(['a','b','c','d'])
b=networkx.current_flow_closeness_centrality(G,normalized=True)
b_answer={'a': 1.0, 'b': 0.6, 'c': 0.6, 'd':0.6}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
class TestWeightedFlowClosenessCentrality(object):
pass
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