1 files changed, 100 insertions, 0 deletions
diff --git a/networkx/algorithms/assortativity/tests/test_correlation.py b/networkx/algorithms/assortativity/tests/test_correlation.py
new file mode 100644
index 00000000..fc61cac9
--- /dev/null
+++ b/networkx/algorithms/assortativity/tests/test_correlation.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python
+from nose.tools import *
+from nose import SkipTest
+import networkx as nx
+from base_test import BaseTestAttributeMixing,BaseTestDegreeMixing
+
+
+class TestDegreeMixingCorrelation(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.')
+        try:
+            import scipy
+            import scipy.stats
+        except ImportError:
+             raise SkipTest('SciPy not available.')
+
+
+
+    def test_degree_assortativity_undirected(self):
+        r=nx.degree_assortativity(self.P4)
+        npt.assert_almost_equal(r,-1.0/2,decimal=4)
+
+    def test_degree_assortativity_directed(self):
+        r=nx.degree_assortativity(self.D)
+        npt.assert_almost_equal(r,-0.57735,decimal=4)
+
+    def test_degree_assortativity_multigraph(self):
+        r=nx.degree_assortativity(self.M)
+        npt.assert_almost_equal(r,-1.0/7.0,decimal=4)
+
+
+    def test_degree_assortativity_undirected(self):
+        r=nx.degree_pearsonr(self.P4)
+        npt.assert_almost_equal(r,-1.0/2,decimal=4)
+
+    def test_degree_assortativity_directed(self):
+        r=nx.degree_pearsonr(self.D)
+        npt.assert_almost_equal(r,-0.57735,decimal=4)
+
+    def test_degree_assortativity_multigraph(self):
+        r=nx.degree_pearsonr(self.M)
+        npt.assert_almost_equal(r,-1.0/7.0,decimal=4)
+
+
+
+class TestAttributeMixingCorrelation(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_assortativity_undirected(self):
+        r=nx.attribute_assortativity(self.G,'fish')
+        assert_equal(r,6.0/22.0)
+
+    def test_attribute_assortativity_directed(self):
+        r=nx.attribute_assortativity(self.D,'fish')
+        assert_equal(r,1.0/3.0)
+
+    def test_attribute_assortativity_multigraph(self):
+        r=nx.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=nx.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=nx.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=nx.attribute_assortativity_coefficient(a)
+        npt.assert_almost_equal(r,0.029,decimal=3)
+