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#!/usr/bin/env python
from nose.tools import *
import nose.tools
import networkx
import networkx.algorithms.mixing as mixing
class TestDegreeMixing(object):
def setUp(self):
self.P4=networkx.path_graph(4)
self.D=networkx.DiGraph()
self.D.add_edges_from([(0, 2), (0, 3), (1, 3), (2, 3)])
self.M=networkx.MultiGraph()
self.M.add_path(range(4))
self.M.add_edge(0,1)
self.S=networkx.Graph()
self.S.add_edges_from([(0,0),(1,1)])
def test_node_degree_xy_undirected(self):
xy=sorted(mixing.node_degree_xy(self.P4))
xy_result=sorted([(1,2),
(2,1),
(2,2),
(2,2),
(1,2),
(2,1)])
assert_equal(xy,xy_result)
def test_node_degree_xy_directed(self):
xy=sorted(mixing.node_degree_xy(self.D))
xy_result=sorted([(2,1),
(2,3),
(1,3),
(1,3)])
assert_equal(xy,xy_result)
def test_node_degree_xy_multigraph(self):
xy=sorted(mixing.node_degree_xy(self.M))
xy_result=sorted([(2,3),
(2,3),
(3,2),
(3,2),
(2,3),
(3,2),
(1,2),
(2,1)])
assert_equal(xy,xy_result)
def test_node_degree_xy_selfloop(self):
xy=sorted(mixing.node_degree_xy(self.S))
xy_result=sorted([(2,2),
(2,2)])
assert_equal(xy,xy_result)
def test_degree_mixing_dict_undirected(self):
d=mixing.degree_mixing_dict(self.P4)
d_result={1:{2:2},
2:{1:2,2:2},
}
assert_equal(d,d_result)
def test_degree_mixing_dict_directed(self):
d=mixing.degree_mixing_dict(self.D)
print d
d_result={1:{3:2},
2:{1:1,3:1},
3:{}
}
assert_equal(d,d_result)
def test_degree_mixing_dict_multigraph(self):
d=mixing.degree_mixing_dict(self.M)
d_result={1:{2:1},
2:{1:1,3:3},
3:{2:3}
}
assert_equal(d,d_result)
class TestDegreeMixingMatrix(object):
@classmethod
def setupClass(cls):
global np
global npt
try:
import numpy as np
import numpy.testing as npt
except ImportError:
raise SkipTest('NumPy not available.')
def setUp(self):
self.P4=networkx.path_graph(4)
self.D=networkx.DiGraph()
self.D.add_edges_from([(0, 2), (0, 3), (1, 3), (2, 3)])
self.M=networkx.MultiGraph()
self.M.add_path(range(4))
self.M.add_edge(0,1)
self.S=networkx.Graph()
self.S.add_edges_from([(0,0),(1,1)])
def test_degree_mixing_matrix_undirected(self):
a_result=np.array([[0,0,0],
[0,0,2],
[0,2,2]]
)
a=mixing.degree_mixing_matrix(self.P4,normalized=False)
npt.assert_equal(a,a_result)
a=mixing.degree_mixing_matrix(self.P4)
npt.assert_equal(a,a_result/float(a_result.sum()))
def test_degree_mixing_matrix_directed(self):
a_result=np.array([[0,0,0,0],
[0,0,0,2],
[0,1,0,1],
[0,0,0,0]]
)
a=mixing.degree_mixing_matrix(self.D,normalized=False)
npt.assert_equal(a,a_result)
a=mixing.degree_mixing_matrix(self.D)
npt.assert_equal(a,a_result/float(a_result.sum()))
def test_degree_mixing_matrix_multigraph(self):
a_result=np.array([[0,0,0,0],
[0,0,1,0],
[0,1,0,3],
[0,0,3,0]]
)
a=mixing.degree_mixing_matrix(self.M,normalized=False)
npt.assert_equal(a,a_result)
a=mixing.degree_mixing_matrix(self.M)
npt.assert_equal(a,a_result/float(a_result.sum()))
def test_degree_mixing_matrix_selfloop(self):
a_result=np.array([[0,0,0],
[0,0,0],
[0,0,2]]
)
a=mixing.degree_mixing_matrix(self.S,normalized=False)
npt.assert_equal(a,a_result)
a=mixing.degree_mixing_matrix(self.S)
npt.assert_equal(a,a_result/float(a_result.sum()))
def test_degree_assortativity_undirected(self):
r=mixing.degree_assortativity(self.P4)
npt.assert_almost_equal(r,-1.0/2,decimal=4)
def test_degree_assortativity_directed(self):
r=mixing.degree_assortativity(self.D)
npt.assert_almost_equal(r,-0.57735,decimal=4)
def test_degree_assortativity_multigraph(self):
r=mixing.degree_assortativity(self.M)
npt.assert_almost_equal(r,-1.0/7.0,decimal=4)
class TestDegreeMixingMatrixPearsonr(object):
@classmethod
def setupClass(cls):
global np
global npt
try:
import numpy as np
import numpy.testing as npt
except ImportError:
raise SkipTest('NumPy not available.')
try:
import scipy
except ImportError:
raise SkipTest('SciPy not available.')
def setUp(self):
self.P4=networkx.path_graph(4)
self.D=networkx.DiGraph()
self.D.add_edges_from([(0, 2), (0, 3), (1, 3), (2, 3)])
self.M=networkx.MultiGraph()
self.M.add_path(range(4))
self.M.add_edge(0,1)
self.S=networkx.Graph()
self.S.add_edges_from([(0,0),(1,1)])
def test_degree_assortativity_undirected(self):
r=mixing.degree_pearsonr(self.P4)
npt.assert_almost_equal(r,-1.0/2,decimal=4)
def test_degree_assortativity_directed(self):
r=mixing.degree_pearsonr(self.D)
npt.assert_almost_equal(r,-0.57735,decimal=4)
def test_degree_assortativity_multigraph(self):
r=mixing.degree_pearsonr(self.M)
npt.assert_almost_equal(r,-1.0/7.0,decimal=4)
class TestNeighborhoodConnectivity():
def setUp(self):
self.P4=networkx.path_graph(4)
self.D=networkx.DiGraph()
self.D.add_edges_from([(0, 2), (0, 3), (1, 3), (2, 3)])
self.M=networkx.MultiGraph()
self.M.add_path(range(4))
self.M.add_edge(0,1)
self.S=networkx.Graph()
self.S.add_edges_from([(0,0),(1,1)])
def test_neighbor_connectivity(self):
d=networkx.neighbor_connectivity(self.P4)
assert_equal(d,{1:2.0/3,2:1.0})
d=networkx.neighbor_connectivity(networkx.complete_graph(4))
assert_equal(d,{3:3.0})
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