#!/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_undirected_normalized(self):
        d=nx.degree_mixing_dict(self.P4, normalized=True)
        d_result={1:{2:1.0/3},
                  2:{1:1.0/3,2:1.0/3},
                  }
        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()))