def test_enspie(self):
# Totally even community should have ENS_pie = number of OTUs.
self.assertAlmostEqual(enspie(np.array([1, 1, 1, 1, 1, 1])), 6)
self.assertAlmostEqual(enspie(np.array([13, 13, 13, 13])), 4)
# Hand calculated.
arr = np.array([1, 41, 0, 0, 12, 13])
exp = 1 / ((arr / arr.sum()) ** 2).sum()
self.assertAlmostEqual(enspie(arr), exp)
# Using dominance.
exp = 1 / dominance(arr)
self.assertAlmostEqual(enspie(arr), exp)
arr = np.array([1, 0, 2, 5, 2])
exp = 1 / dominance(arr)
self.assertAlmostEqual(enspie(arr), exp)
def test_enspie(self):
# Totally even community should have ENS_pie = number of OTUs.
self.assertAlmostEqual(enspie(np.array([1, 1, 1, 1, 1, 1])), 6)
self.assertAlmostEqual(enspie(np.array([13, 13, 13, 13])), 4)
# Hand calculated.
arr = np.array([1, 41, 0, 0, 12, 13])
exp = 1 / ((arr / arr.sum())**2).sum()
self.assertAlmostEqual(enspie(arr), exp)
# Using dominance.
exp = 1 / dominance(arr)
self.assertAlmostEqual(enspie(arr), exp)
arr = np.array([1, 0, 2, 5, 2])
exp = 1 / dominance(arr)
self.assertAlmostEqual(enspie(arr), exp)
def test_dominance(self):
self.assertEqual(dominance(np.array([5])), 1)
self.assertAlmostEqual(dominance(np.array([1, 0, 2, 5, 2])), 0.34)
def test_dominance(self):
self.assertEqual(dominance(np.array([5])), 1)
self.assertAlmostEqual(dominance(np.array([1, 0, 2, 5, 2])), 0.34)