def test_missing_values(self):
md = qiime2.MetadataCategory(
pd.Series([1.0, 2.0, np.nan, 4.0],
name='number',
index=['sample1', 'sample2', 'sample3', 'sample4']))
with self.assertRaisesRegex(ValueError, 'missing values'):
distance_matrix(md)
def test_non_numeric_category(self):
md = qiime2.MetadataCategory(
pd.Series(['x1', 'x2', '3', '4'],
name='number',
index=['sample1', 'sample2', 'sample3', 'sample4']))
with self.assertRaisesRegex(ValueError,
'non-numeric values.*\n\n.*x1'):
distance_matrix(md)
def test_missing_values(self):
md = qiime2.NumericMetadataColumn(
pd.Series([1.0, 2.0, np.nan, 4.0],
name='number',
index=pd.Index(
['sample1', 'sample2', 'sample3', 'sample4'],
name='id')))
with self.assertRaisesRegex(ValueError, 'missing values'):
distance_matrix(md)
def test_one_sample(self):
md = qiime2.MetadataCategory(
pd.Series([1.5], name='number', index=['sample1']))
exp = skbio.DistanceMatrix([[0.0]], ids=['sample1'])
obs = distance_matrix(md)
self.assertEqual(exp, obs)
def test_one_sample(self):
md = qiime2.NumericMetadataColumn(
pd.Series([1.5],
name='number',
index=pd.Index(['sample1'], name='id')))
exp = skbio.DistanceMatrix([[0.0]], ids=['sample1'])
obs = distance_matrix(md)
self.assertEqual(exp, obs)
def test_float_category(self):
md = qiime2.MetadataCategory(
pd.Series([1.5, 2.0, 3.0],
name='number',
index=['sample1', 'sample2', 'sample3']))
exp = skbio.DistanceMatrix(
[[0.0, 0.5, 1.5], [0.5, 0.0, 1.0], [1.5, 1.0, 0.0]],
ids=['sample1', 'sample2', 'sample3'])
obs = distance_matrix(md)
self.assertEqual(exp, obs)
def test_int_category(self):
md = qiime2.MetadataCategory(
pd.Series([1, 2, 3],
name='number',
index=['sample1', 'sample2', 'sample3']))
exp = skbio.DistanceMatrix([[0, 1, 2], [1, 0, 1], [2, 1, 0]],
ids=['sample1', 'sample2', 'sample3'])
obs = distance_matrix(md)
self.assertEqual(exp, obs)
def test_float_column(self):
md = qiime2.NumericMetadataColumn(
pd.Series([1.5, 2.0, 3.0],
name='number',
index=pd.Index(['sample1', 'sample2', 'sample3'],
name='id')))
exp = skbio.DistanceMatrix(
[[0.0, 0.5, 1.5], [0.5, 0.0, 1.0], [1.5, 1.0, 0.0]],
ids=['sample1', 'sample2', 'sample3'])
obs = distance_matrix(md)
self.assertEqual(exp, obs)
def test_int_column(self):
md = qiime2.NumericMetadataColumn(
pd.Series([1, 2, 3],
name='number',
index=pd.Index(['sample1', 'sample2', 'sample3'],
name='id')))
exp = skbio.DistanceMatrix([[0, 1, 2], [1, 0, 1], [2, 1, 0]],
ids=['sample1', 'sample2', 'sample3'])
obs = distance_matrix(md)
self.assertEqual(exp, obs)
def test_str_casting(self):
md = qiime2.MetadataCategory(
pd.Series(['1', '2', '3', '4'],
name='number',
index=['sample1', 'sample2', 'sample3', 'sample4']))
exp = skbio.DistanceMatrix(
[[0.0, 1.0, 2.0, 3.0], [1.0, 0.0, 1.0, 2.0], [2.0, 1.0, 0.0, 1.0],
[3.0, 2.0, 1.0, 0.0]],
ids=['sample1', 'sample2', 'sample3', 'sample4'])
obs = distance_matrix(md)
self.assertEqual(exp, obs)
def test_empty_metadata_category(self):
md = qiime2.MetadataCategory(pd.Series([], name='number', index=[]))
with self.assertRaisesRegex(ValueError, 'metadata category.*empty'):
distance_matrix(md)
