def test_different_trees(self): np.random.seed(1234) # remove randomness for sake of testing p-value coeff, p_value, n = compare_tip_to_tip_distances(self.tree1, self.tree2) self.assertAlmostEqual(coeff, 0.59603956067926978) self.assertAlmostEqual(p_value, 0.69599999999999995) self.assertEqual(n, 3)
def test_different_trees(self): np.random.seed(1234) # remove randomness for sake of testing p-value coeff, p_value, n = compare_tip_to_tip_distances(self.tree1, self.tree2) self.assertAlmostEqual(coeff, 0.59603956067926978) self.assertAlmostEqual(p_value, 0.69599999999999995) self.assertEqual(n, 3)
def tip_to_tip_distances(output_dir: str, tree_1: NewickFormat, tree_2: NewickFormat, method: str=_ghost_tree_defaults['method']): tree1_fh = tree_1.open() tree2_fh = tree_2.open() stats_results = compare_tip_to_tip_distances( tree1_fh, tree2_fh, method) data_dict = { 'Correlation Coefficient': str(round(stats_results[0], 5)), 'p-value': str(stats_results[1]), 'Number of Overlapping Tips': str(stats_results[2]), } df = pd.Series(data=data_dict).to_frame() df.columns = ['Tree Comparison Statistics'] index = os.path.join(output_dir, 'index.html') with open(index, 'w') as fh: fh.write(df.to_html())
def test_same_trees(self): np.random.seed(1234) # remove randomness for sake of testing p-value result = compare_tip_to_tip_distances(self.tree1, self.tree1_copy) self.assertEqual(result, (1.0, 0.042, 4))
def test_same_trees(self): np.random.seed(1234) # remove randomness for sake of testing p-value result = compare_tip_to_tip_distances(self.tree1, self.tree1_copy) self.assertEqual(result, (1.0, 0.042, 4))