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))
