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#!/usr/bin/env python
from nose.tools import *
import networkx
import networkx.algorithms.mixing as mixing
class TestAttributeMixing(object):
def setUp(self):
G=networkx.Graph()
G.add_nodes_from([0,1],fish='one')
G.add_nodes_from([2,3],fish='two')
G.add_nodes_from([4],fish='red')
G.add_nodes_from([5],fish='blue')
G.add_edges_from([(0,1),(2,3),(0,4),(2,5)])
self.G=G
D=networkx.DiGraph()
D.add_nodes_from([0,1],fish='one')
D.add_nodes_from([2,3],fish='two')
D.add_nodes_from([4],fish='red')
D.add_nodes_from([5],fish='blue')
D.add_edges_from([(0,1),(2,3),(0,4),(2,5)])
self.D=D
M=networkx.MultiGraph()
M.add_nodes_from([0,1],fish='one')
M.add_nodes_from([2,3],fish='two')
M.add_nodes_from([4],fish='red')
M.add_nodes_from([5],fish='blue')
M.add_edges_from([(0,1),(0,1),(2,3)])
self.M=M
S=networkx.Graph()
S.add_nodes_from([0,1],fish='one')
S.add_nodes_from([2,3],fish='two')
S.add_nodes_from([4],fish='red')
S.add_nodes_from([5],fish='blue')
S.add_edge(0,0)
S.add_edge(2,2)
self.S=S
def test_node_attribute_xy_undirected(self):
attrxy=sorted(mixing.node_attribute_xy(self.G,'fish'))
attrxy_result=sorted([('one','one'),
('one','one'),
('two','two'),
('two','two'),
('one','red'),
('red','one'),
('blue','two'),
('two','blue')
])
assert_equal(attrxy,attrxy_result)
def test_node_attribute_xy_directed(self):
attrxy=sorted(mixing.node_attribute_xy(self.D,'fish'))
attrxy_result=sorted([('one','one'),
('two','two'),
('one','red'),
('two','blue')
])
assert_equal(attrxy,attrxy_result)
def test_node_attribute_xy_multigraph(self):
attrxy=sorted(mixing.node_attribute_xy(self.M,'fish'))
attrxy_result=[('one','one'),
('one','one'),
('one','one'),
('one','one'),
('two','two'),
('two','two')
]
assert_equal(attrxy,attrxy_result)
def test_node_attribute_xy_selfloop(self):
attrxy=sorted(mixing.node_attribute_xy(self.S,'fish'))
attrxy_result=[('one','one'),
('two','two')
]
assert_equal(attrxy,attrxy_result)
def test_attribute_mixing_dict_undirected(self):
d=mixing.attribute_mixing_dict(self.G,'fish')
d_result={'one':{'one':2,'red':1},
'two':{'two':2,'blue':1},
'red':{'one':1},
'blue':{'two':1}
}
assert_equal(d,d_result)
def test_attribute_mixing_dict_directed(self):
d=mixing.attribute_mixing_dict(self.D,'fish')
d_result={'one':{'one':1,'red':1},
'two':{'two':1,'blue':1},
'red':{},
'blue':{}
}
assert_equal(d,d_result)
def test_attribute_mixing_dict_multigraph(self):
d=mixing.attribute_mixing_dict(self.M,'fish')
d_result={'one':{'one':4},
'two':{'two':2},
}
assert_equal(d,d_result)
class TestAttributeMixingMatrix(TestAttributeMixing):
@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 test_attribute_mixing_matrix_undirected(self):
mapping={'one':0,'two':1,'red':2,'blue':3}
a_result=np.array([[2,0,1,0],
[0,2,0,1],
[1,0,0,0],
[0,1,0,0]]
)
a=mixing.attribute_mixing_matrix(self.G,'fish',
mapping=mapping,
normalized=False)
npt.assert_equal(a,a_result)
a=mixing.attribute_mixing_matrix(self.G,'fish',
mapping=mapping)
npt.assert_equal(a,a_result/float(a_result.sum()))
def test_attribute_mixing_matrix_directed(self):
mapping={'one':0,'two':1,'red':2,'blue':3}
a_result=np.array([[1,0,1,0],
[0,1,0,1],
[0,0,0,0],
[0,0,0,0]]
)
a=mixing.attribute_mixing_matrix(self.D,'fish',
mapping=mapping,
normalized=False)
npt.assert_equal(a,a_result)
a=mixing.attribute_mixing_matrix(self.D,'fish',
mapping=mapping)
npt.assert_equal(a,a_result/float(a_result.sum()))
def test_attribute_mixing_matrix_multigraph(self):
mapping={'one':0,'two':1,'red':2,'blue':3}
a_result=np.array([[4,0,0,0],
[0,2,0,0],
[0,0,0,0],
[0,0,0,0]]
)
a=mixing.attribute_mixing_matrix(self.M,'fish',
mapping=mapping,
normalized=False)
npt.assert_equal(a,a_result)
a=mixing.attribute_mixing_matrix(self.M,'fish',
mapping=mapping)
npt.assert_equal(a,a_result/float(a_result.sum()))
def test_attribute_assortativity_undirected(self):
r=mixing.attribute_assortativity(self.G,'fish')
assert_equal(r,6.0/22.0)
def test_attribute_assortativity_directed(self):
r=mixing.attribute_assortativity(self.D,'fish')
assert_equal(r,1.0/3.0)
def test_attribute_assortativity_multigraph(self):
r=mixing.attribute_assortativity(self.M,'fish')
assert_equal(r,1.0)
def test_attribute_assortativity_coefficient(self):
# from "Mixing patterns in networks"
a=np.array([[0.258,0.016,0.035,0.013],
[0.012,0.157,0.058,0.019],
[0.013,0.023,0.306,0.035],
[0.005,0.007,0.024,0.016]])
r=mixing.attribute_assortativity_coefficient(a)
npt.assert_almost_equal(r,0.623,decimal=3)
def test_attribute_assortativity_coefficient(self):
a=np.array([[0.18,0.02,0.01,0.03],
[0.02,0.20,0.03,0.02],
[0.01,0.03,0.16,0.01],
[0.03,0.02,0.01,0.22]])
r=mixing.attribute_assortativity_coefficient(a)
npt.assert_almost_equal(r,0.68,decmial=2)
def test_attribute_assortativity_coefficient(self):
a=np.array([[50,50,0],[50,50,0],[0,0,2]])
r=mixing.attribute_assortativity_coefficient(a)
npt.assert_almost_equal(r,0.029,decimal=3)
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