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#!/usr/bin/env python
from nose.tools import *
from nose import SkipTest
import networkx as nx
from base_test import BaseTestAttributeMixing,BaseTestDegreeMixing
class TestDegreeMixingDict(BaseTestDegreeMixing):
def test_degree_mixing_dict_undirected(self):
d=nx.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=nx.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=nx.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(BaseTestDegreeMixing):
@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_degree_mixing_matrix_undirected(self):
a_result=np.array([[0,0,0],
[0,0,2],
[0,2,2]]
)
a=nx.degree_mixing_matrix(self.P4,normalized=False)
npt.assert_equal(a,a_result)
a=nx.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=nx.degree_mixing_matrix(self.D,normalized=False)
npt.assert_equal(a,a_result)
a=nx.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=nx.degree_mixing_matrix(self.M,normalized=False)
npt.assert_equal(a,a_result)
a=nx.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=nx.degree_mixing_matrix(self.S,normalized=False)
npt.assert_equal(a,a_result)
a=nx.degree_mixing_matrix(self.S)
npt.assert_equal(a,a_result/float(a_result.sum()))
class TestAttributeMixingDict(BaseTestAttributeMixing):
def test_attribute_mixing_dict_undirected(self):
d=nx.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=nx.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=nx.attribute_mixing_dict(self.M,'fish')
d_result={'one':{'one':4},
'two':{'two':2},
}
assert_equal(d,d_result)
class TestAttributeMixingMatrix(BaseTestAttributeMixing):
@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=nx.attribute_mixing_matrix(self.G,'fish',
mapping=mapping,
normalized=False)
npt.assert_equal(a,a_result)
a=nx.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=nx.attribute_mixing_matrix(self.D,'fish',
mapping=mapping,
normalized=False)
npt.assert_equal(a,a_result)
a=nx.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=nx.attribute_mixing_matrix(self.M,'fish',
mapping=mapping,
normalized=False)
npt.assert_equal(a,a_result)
a=nx.attribute_mixing_matrix(self.M,'fish',
mapping=mapping)
npt.assert_equal(a,a_result/float(a_result.sum()))
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