def test_empty_table(self):
mc = qiime2.MetadataCategory(
pd.Series(['a_new', 'b_new'], index=['a', 'b']))
table = biom.Table(np.array([[]]), sample_ids=[], observation_ids=[])
with self.assertRaisesRegex(ValueError, 'empty table'):
group(table, axis='sample', metadata=mc, mode='sum')
with self.assertRaisesRegex(ValueError, 'empty table'):
group(table, axis='feature', metadata=mc, mode='sum')
def test_empty_table(self):
mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', 'b_new'], name='foo',
index=pd.Index(['a', 'b'], name='id')))
table = biom.Table(np.array([[]]), sample_ids=[], observation_ids=[])
with self.assertRaisesRegex(ValueError, 'empty table'):
group(table, axis='sample', metadata=mc, mode='sum')
with self.assertRaisesRegex(ValueError, 'empty table'):
group(table, axis='feature', metadata=mc, mode='sum')
def test_missing_feature_ids(self):
feature_mc = qiime2.MetadataCategory(
pd.Series(['g0', 'g1', 'g2', 'g1', 'g2', 'extra'],
index=['a', 'c', 'd', 'e', 'f', 'g']))
data = np.array([[1, 0, 0], [1, 10, 10], [0, 0, 100], [5, 5, 5],
[0, 1, 100], [7, 8, 9]])
# g is missing on purpose
table = biom.Table(data,
sample_ids=['s1', 's2', 's3'],
observation_ids=['a', 'b', 'c', 'd', 'e', 'f'])
with self.assertRaisesRegex(ValueError, 'metadata.*missing: {\'b\'}'):
group(table, axis='feature', metadata=feature_mc, mode='sum')
def test_missing_sample_ids(self):
sample_mc = qiime2.MetadataCategory(
pd.Series(['g0', 'g2', 'g0', 'g2'], index=['s1', 's3', 's4',
's6']))
data = np.array([[0, 1, 2, 3], [10, 11, 12, 13], [100, 110, 120, 130]])
table = biom.Table(data,
sample_ids=['s1', 's2', 's4', 's5'],
observation_ids=['x', 'y', 'z'])
with self.assertRaisesRegex(ValueError, 'metadata.*missing:') as e:
group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertIn('s2', str(e.exception))
self.assertIn('s5', str(e.exception))
def test_missing_feature_ids(self):
feature_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['g0', 'g1', 'g2', 'g1', 'g2', 'extra'],
name='foo',
index=pd.Index(['a', 'c', 'd', 'e', 'f', 'g'],
name='featureid')))
data = np.array([[1, 0, 0], [1, 10, 10], [0, 0, 100], [5, 5, 5],
[0, 1, 100], [7, 8, 9]])
# g is missing on purpose
table = biom.Table(data,
sample_ids=['s1', 's2', 's3'],
observation_ids=['a', 'b', 'c', 'd', 'e', 'f'])
with self.assertRaisesRegex(ValueError, "not present.*'b'"):
group(table, axis='feature', metadata=feature_mc, mode='sum')
def test_missing_sample_ids(self):
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['g0', 'g2', 'g0', 'g2'],
name='foo',
index=pd.Index(['s1', 's3', 's4', 's6'],
name='sampleid')))
data = np.array([[0, 1, 2, 3], [10, 11, 12, 13], [100, 110, 120, 130]])
table = biom.Table(data,
sample_ids=['s1', 's2', 's4', 's5'],
observation_ids=['x', 'y', 'z'])
with self.assertRaisesRegex(ValueError, 'not present.*s2.*s5') as e:
group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertIn('s2', str(e.exception))
self.assertIn('s5', str(e.exception))
def group_by_transect(table, tree, distance):
otu_map = collapse_tree(tree, distance)
use_names = not (not tree['names'][1] or is_float(tree['names'][1]))
def name(otu):
if use_names:
i = otu
names = []
while i != 0:
names.append(tree['names'][i])
i = tree['parent'][i]
if names:
return '; '.join(reversed(names))
return "T_%s:%d" % (distance, otu)
groups = pd.Series(
{
tree['names'][tip]: name(otu)
for otu, tips in otu_map.items() for tip in tips
},
name='T_%s' % distance).sort_index()
groups.index.name = 'id'
collapsed_counts = pd.Series(
{name(otu): len(tips)
for otu, tips in otu_map.items()},
name='Cluster Size').sort_index()
column = q2.CategoricalMetadataColumn(groups)
collapsed_table = group(table, axis='feature', metadata=column, mode='sum')
return collapsed_table, collapsed_counts, groups
def test_missing_sample_ids(self):
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['g0', 'g2', 'g0', 'g2'], name='foo',
index=pd.Index(['s1', 's3', 's4', 's6'],
name='sampleid')))
data = np.array([
[0, 1, 2, 3],
[10, 11, 12, 13],
[100, 110, 120, 130]])
table = biom.Table(data, sample_ids=['s1', 's2', 's4', 's5'],
observation_ids=['x', 'y', 'z'])
with self.assertRaisesRegex(ValueError, 'not present.*s2.*s5') as e:
group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertIn('s2', str(e.exception))
self.assertIn('s5', str(e.exception))
def test_feature_sum(self):
sample_mc, feature_mc, table = self._shared_setup()
expected = biom.Table(np.array([[0, 1, 11, 111, 1112, 20, 22],
[23, 14, 25, 11, 11, 104, 36]]),
sample_ids=sample_mc.to_series().index,
observation_ids=['g0', 'g1'])
result = group(table, axis='feature', metadata=feature_mc, mode='sum')
self.assertEqual(expected, result)
def test_sample_sum(self):
sample_mc, feature_mc, table = self._shared_setup()
expected = biom.Table(np.array([[0, 1, 2], [20, 20, 111], [15, 14, 37],
[2, 2, 3], [1, 1122, 151]]),
sample_ids=['treatment', 'control', 'other'],
observation_ids=feature_mc.to_series().index)
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
def test_missing_feature_ids(self):
feature_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['g0', 'g1', 'g2', 'g1', 'g2', 'extra'], name='foo',
index=pd.Index(['a', 'c', 'd', 'e', 'f', 'g'],
name='featureid')))
data = np.array([
[1, 0, 0],
[1, 10, 10],
[0, 0, 100],
[5, 5, 5],
[0, 1, 100],
[7, 8, 9]])
# g is missing on purpose
table = biom.Table(data, sample_ids=['s1', 's2', 's3'],
observation_ids=['a', 'b', 'c', 'd', 'e', 'f'])
with self.assertRaisesRegex(ValueError, "not present.*'b'"):
group(table, axis='feature', metadata=feature_mc, mode='sum')
def test_feature_sum(self):
sample_mc, feature_mc, table = self._shared_setup()
expected = biom.Table(
np.array([[0, 1, 11, 111, 1112, 20, 22],
[23, 14, 25, 11, 11, 104, 36]]),
sample_ids=sample_mc.to_series().index,
observation_ids=['g0', 'g1'])
result = group(table, axis='feature', metadata=feature_mc, mode='sum')
self.assertEqual(expected, result)
def test_feature_median_ceiling(self):
sample_mc, feature_mc, table = self._shared_setup()
expected = biom.Table(
np.array([[0, 1, 6, 56, 556, 10, 11],
[10, 3, 10, 1, 1, 3, 1]]),
sample_ids=sample_mc.to_series().index,
observation_ids=['g0', 'g1'])
result = group(table, axis='feature', metadata=feature_mc,
mode='median-ceiling')
self.assertEqual(expected, result)
def test_feature_median_ceiling(self):
sample_mc, feature_mc, table = self._shared_setup()
expected = biom.Table(
np.array([[0, 1, 6, 56, 556, 10, 11],
[10, 3, 10, 1, 1, 3, 1]]),
sample_ids=sample_mc.to_series().index,
observation_ids=['g0', 'g1'])
result = group(table, axis='feature', metadata=feature_mc,
mode='median-ceiling')
self.assertEqual(expected, result)
def test_empty_metadata_values(self):
# Trusting that the code is sane enough to not invent a distinction
# between feature and sample metadata where there is none
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', 'a_new', None], name='foo',
index=pd.Index(['a', 'b', 'c'], name='sampleid')))
sample_ids = sample_mc.to_series().index
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data, sample_ids=sample_ids,
observation_ids=['x', 'y'])
with self.assertRaisesRegex(ValueError, "missing.*value.*'c'"):
group(table, axis='sample', metadata=sample_mc, mode='sum')
nan_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', float('nan'), 'a_new'], name='foo',
index=pd.Index(['a', 'b', 'c'], name='id')))
with self.assertRaisesRegex(ValueError, "missing.*value.*'b'"):
group(table, axis='sample', metadata=nan_mc, mode='sum')
def test_empty_metadata_values(self):
# Trusting that the code is sane enough to not invent a distinction
# between feature and sample metadata where there is none
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', 'a_new', None], name='foo',
index=pd.Index(['a', 'b', 'c'], name='sampleid')))
sample_ids = sample_mc.to_series().index
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data, sample_ids=sample_ids,
observation_ids=['x', 'y'])
with self.assertRaisesRegex(ValueError, "missing.*value.*'c'"):
group(table, axis='sample', metadata=sample_mc, mode='sum')
nan_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', float('nan'), 'a_new'], name='foo',
index=pd.Index(['a', 'b', 'c'], name='id')))
with self.assertRaisesRegex(ValueError, "missing.*value.*'b'"):
group(table, axis='sample', metadata=nan_mc, mode='sum')
def test_sample_median_ceiling(self):
sample_mc, feature_mc, table = self._shared_setup()
expected = biom.Table(np.array([[0, 1, 0], [10, 10, 10], [8, 7, 3],
[1, 1, 1], [1, 561, 20]]),
sample_ids=['treatment', 'control', 'other'],
observation_ids=feature_mc.to_series().index)
result = group(table,
axis='sample',
metadata=sample_mc,
mode='median-ceiling')
self.assertEqual(expected, result)
def test_numeric(self):
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data,
sample_ids=['a', 'b', 'c'],
observation_ids=['x', 'y'])
# ints
sample_mc = qiime2.MetadataCategory(
pd.Series(['1', '2', '3'], index=['a', 'b', 'c']))
with self.assertRaisesRegex(ValueError, 'numeric'):
group(table, axis='sample', metadata=sample_mc, mode='sum')
# floats
sample_mc = qiime2.MetadataCategory(
pd.Series(['1.1', '2.2', '3.3333'], index=['a', 'b', 'c']))
with self.assertRaisesRegex(ValueError, 'numeric'):
group(table, axis='sample', metadata=sample_mc, mode='sum')
# mixed
sample_mc = qiime2.MetadataCategory(
pd.Series(['0', '42', '4.2'], index=['a', 'b', 'c']))
with self.assertRaisesRegex(ValueError, 'numeric'):
group(table, axis='sample', metadata=sample_mc, mode='sum')
def test_empty_metadata_values(self):
# Trusting that the code is sane enough to not invent a distinction
# between feature and sample metadata where there is none
sample_mc = qiime2.MetadataCategory(
pd.Series(['a_new', 'a_new', None], index=['a', 'b', 'c']))
sample_ids = sample_mc.to_series().index
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data,
sample_ids=sample_ids,
observation_ids=['x', 'y'])
with self.assertRaisesRegex(ValueError, 'missing.*value.*{\'c\'}'):
group(table, axis='sample', metadata=sample_mc, mode='sum')
nan_mc = qiime2.MetadataCategory(
pd.Series(['a_new', float('nan'), 'a_new'], index=['a', 'b', 'c']))
with self.assertRaisesRegex(ValueError, 'missing.*value.*{\'b\'}'):
group(table, axis='sample', metadata=nan_mc, mode='sum')
empty_str = qiime2.MetadataCategory(
pd.Series(['', 'y_new'], index=['x', 'y']))
with self.assertRaisesRegex(ValueError, 'missing.*value.*{\'x\'}'):
group(table,
axis='feature',
metadata=empty_str,
mode='median-ceiling')
def test_sample_sum(self):
sample_mc, feature_mc, table = self._shared_setup()
expected = biom.Table(
np.array([[0, 1, 2],
[20, 20, 111],
[15, 14, 37],
[2, 2, 3],
[1, 1122, 151]]),
sample_ids=['treatment', 'control', 'other'],
observation_ids=feature_mc.to_series().index)
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
def test_numeric_strings(self):
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data, sample_ids=['a', 'b', 'c'],
observation_ids=['x', 'y'])
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['-4.2', '-4.2', '-4.2'], name='foo',
index=pd.Index(['a', 'b', 'c'], name='sampleid')))
expected = biom.Table(np.array([[6], [60]]),
sample_ids=['-4.2'],
observation_ids=['x', 'y'])
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
def test_sample_median_ceiling(self):
sample_mc, feature_mc, table = self._shared_setup()
expected = biom.Table(
np.array([[0, 1, 0],
[10, 10, 10],
[8, 7, 3],
[1, 1, 1],
[1, 561, 20]]),
sample_ids=['treatment', 'control', 'other'],
observation_ids=feature_mc.to_series().index)
result = group(table, axis='sample', metadata=sample_mc,
mode='median-ceiling')
self.assertEqual(expected, result)
def test_reorder(self):
sample_mc = qiime2.MetadataCategory(
pd.Series(['c', 'b', 'a'], index=['c', 'b', 'a']))
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data,
sample_ids=['a', 'b', 'c'],
observation_ids=['x', 'y'])
expected = biom.Table(np.array([[3, 2, 1], [10, 20, 30]]),
sample_ids=['c', 'b', 'a'],
observation_ids=['x', 'y'])
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
def test_numeric_strings(self):
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data, sample_ids=['a', 'b', 'c'],
observation_ids=['x', 'y'])
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['-4.2', '-4.2', '-4.2'], name='foo',
index=pd.Index(['a', 'b', 'c'], name='sampleid')))
expected = biom.Table(np.array([[6], [60]]),
sample_ids=['-4.2'],
observation_ids=['x', 'y'])
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
def test_superset_sample_group(self):
sample_mc = qiime2.MetadataCategory(
pd.Series(['g0', 'g1', 'g2', 'g0', 'g1', 'g2'],
index=['s1', 's2', 's3', 's4', 's5', 's6']))
data = np.array([[0, 1, 2, 3], [10, 11, 12, 13], [100, 110, 120, 130]])
table = biom.Table(data,
sample_ids=['s1', 's2', 's4', 's5'],
observation_ids=['x', 'y', 'z'])
expected = biom.Table(np.array([[2, 4], [22, 24], [220, 240]]),
sample_ids=['g0', 'g1'],
observation_ids=['x', 'y', 'z'])
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
def test_empty_only_in_superset(self):
# Trusting that the code is sane enough to not invent a distinction
# between feature and sample metadata where there is none
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', 'a_new', 'b_new', None], name='foo',
index=pd.Index(['a', 'b', 'c', 'd'], name='sampleid')))
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data, sample_ids=['a', 'b', 'c'],
observation_ids=['x', 'y'])
expected = biom.Table(np.array([[2, 3], [25, 10]]),
sample_ids=['a_new', 'b_new'],
observation_ids=['x', 'y'])
result = group(table, axis='sample', metadata=sample_mc,
mode='mean-ceiling')
self.assertEqual(expected, result)
def test_empty_only_in_superset(self):
# Trusting that the code is sane enough to not invent a distinction
# between feature and sample metadata where there is none
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', 'a_new', 'b_new', None], name='foo',
index=pd.Index(['a', 'b', 'c', 'd'], name='sampleid')))
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data, sample_ids=['a', 'b', 'c'],
observation_ids=['x', 'y'])
expected = biom.Table(np.array([[2, 3], [25, 10]]),
sample_ids=['a_new', 'b_new'],
observation_ids=['x', 'y'])
result = group(table, axis='sample', metadata=sample_mc,
mode='mean-ceiling')
self.assertEqual(expected, result)
def test_superset_feature_group(self):
feature_mc = qiime2.MetadataCategory(
pd.Series(['g0', 'g0', 'g1', 'g2', 'g1', 'g2', 'extra'],
index=['a', 'b', 'c', 'd', 'e', 'f', 'g']))
data = np.array([[1, 0, 0], [1, 10, 10], [0, 0, 100], [5, 5, 5],
[0, 1, 100], [7, 8, 9]])
# g is missing on purpose
table = biom.Table(data,
sample_ids=['s1', 's2', 's3'],
observation_ids=['a', 'b', 'c', 'd', 'e', 'f'])
expected = biom.Table(np.array([[2, 10, 10], [0, 1, 200], [12, 13,
14]]),
sample_ids=['s1', 's2', 's3'],
observation_ids=['g0', 'g1', 'g2'])
result = group(table, axis='feature', metadata=feature_mc, mode='sum')
self.assertEqual(expected, result)
def test_one_to_one_rename(self):
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', 'b_new', 'c_new'], name='foo',
index=pd.Index(['a', 'b', 'c'], name='sampleid')))
original_sample_ids = sample_mc.to_series().index
new_sample_ids = list(sample_mc.to_series())
feature_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['x_new', 'y_new'], name='foo',
index=pd.Index(['x', 'y'], name='featureid')))
original_feature_ids = feature_mc.to_series().index
new_feature_ids = list(feature_mc.to_series())
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data, sample_ids=original_sample_ids,
observation_ids=original_feature_ids)
# Sample renames
expected = biom.Table(data, sample_ids=new_sample_ids,
observation_ids=original_feature_ids)
# Sample x Sum
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
# Sample X Mean
result = group(table, axis='sample', metadata=sample_mc,
mode='mean-ceiling')
self.assertEqual(expected, result)
# Sample X Mean
result = group(table, axis='sample', metadata=sample_mc,
mode='median-ceiling')
self.assertEqual(expected, result)
# Feature renames
expected = biom.Table(data, sample_ids=original_sample_ids,
observation_ids=new_feature_ids)
# Feature X Sum
result = group(table, axis='feature', metadata=feature_mc, mode='sum')
self.assertEqual(expected, result)
# Feature X Mean
result = group(table, axis='feature', metadata=feature_mc,
mode='mean-ceiling')
self.assertEqual(expected, result)
# Feature X Median
result = group(table, axis='feature', metadata=feature_mc,
mode='median-ceiling')
self.assertEqual(expected, result)
def test_one_to_one_rename(self):
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', 'b_new', 'c_new'], name='foo',
index=pd.Index(['a', 'b', 'c'], name='sampleid')))
original_sample_ids = sample_mc.to_series().index
new_sample_ids = list(sample_mc.to_series())
feature_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['x_new', 'y_new'], name='foo',
index=pd.Index(['x', 'y'], name='featureid')))
original_feature_ids = feature_mc.to_series().index
new_feature_ids = list(feature_mc.to_series())
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data, sample_ids=original_sample_ids,
observation_ids=original_feature_ids)
# Sample renames
expected = biom.Table(data, sample_ids=new_sample_ids,
observation_ids=original_feature_ids)
# Sample x Sum
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
# Sample X Mean
result = group(table, axis='sample', metadata=sample_mc,
mode='mean-ceiling')
self.assertEqual(expected, result)
# Sample X Mean
result = group(table, axis='sample', metadata=sample_mc,
mode='median-ceiling')
self.assertEqual(expected, result)
# Feature renames
expected = biom.Table(data, sample_ids=original_sample_ids,
observation_ids=new_feature_ids)
# Feature X Sum
result = group(table, axis='feature', metadata=feature_mc, mode='sum')
self.assertEqual(expected, result)
# Feature X Mean
result = group(table, axis='feature', metadata=feature_mc,
mode='mean-ceiling')
self.assertEqual(expected, result)
# Feature X Median
result = group(table, axis='feature', metadata=feature_mc,
mode='median-ceiling')
self.assertEqual(expected, result)
def test_superset_sample_group(self):
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['g0', 'g1', 'g2', 'g0', 'g1', 'g2'], name='foo',
index=pd.Index(['s1', 's2', 's3', 's4', 's5', 's6'],
name='sampleid')))
data = np.array([
[0, 1, 2, 3],
[10, 11, 12, 13],
[100, 110, 120, 130]])
table = biom.Table(data, sample_ids=['s1', 's2', 's4', 's5'],
observation_ids=['x', 'y', 'z'])
expected = biom.Table(
np.array([[2, 4], [22, 24], [220, 240]]),
sample_ids=['g0', 'g1'],
observation_ids=['x', 'y', 'z'])
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
def test_identity_groups(self):
# These map to the same values as before
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a', 'b', 'c'],
name='foo',
index=pd.Index(['a', 'b', 'c'], name='sampleid')))
feature_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['x', 'y'],
name='foo',
index=pd.Index(['x', 'y'], name='featureid')))
table = biom.Table(np.array([[1, 2, 3], [30, 20, 10]]),
sample_ids=sample_mc.to_series().index,
observation_ids=feature_mc.to_series().index)
# Sample x Sum
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(table, result)
# Sample x Mean
result = group(table,
axis='sample',
metadata=sample_mc,
mode='mean-ceiling')
self.assertEqual(table, result)
# Sample x Median
result = group(table,
axis='sample',
metadata=sample_mc,
mode='median-ceiling')
self.assertEqual(table, result)
# Feature x Sum
result = group(table, axis='feature', metadata=feature_mc, mode='sum')
self.assertEqual(table, result)
# Feature x Mean
result = group(table,
axis='feature',
metadata=feature_mc,
mode='mean-ceiling')
self.assertEqual(table, result)
# Feature x Median
result = group(table,
axis='feature',
metadata=feature_mc,
mode='median-ceiling')
self.assertEqual(table, result)
def test_superset_feature_group(self):
feature_mc = qiime2.CategoricalMetadataColumn(
pd.Series(
['g0', 'g0', 'g1', 'g2', 'g1', 'g2', 'extra'], name='foo',
index=pd.Index(['a', 'b', 'c', 'd', 'e', 'f', 'g'],
name='featureid')))
data = np.array([
[1, 0, 0],
[1, 10, 10],
[0, 0, 100],
[5, 5, 5],
[0, 1, 100],
[7, 8, 9]])
# g is missing on purpose
table = biom.Table(data, sample_ids=['s1', 's2', 's3'],
observation_ids=['a', 'b', 'c', 'd', 'e', 'f'])
expected = biom.Table(
np.array([[2, 10, 10], [0, 1, 200], [12, 13, 14]]),
sample_ids=['s1', 's2', 's3'],
observation_ids=['g0', 'g1', 'g2'])
result = group(table, axis='feature', metadata=feature_mc, mode='sum')
self.assertEqual(expected, result)
def test_identity_groups(self):
# These map to the same values as before
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a', 'b', 'c'], name='foo',
index=pd.Index(['a', 'b', 'c'], name='sampleid')))
feature_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['x', 'y'], name='foo',
index=pd.Index(['x', 'y'], name='featureid')))
table = biom.Table(np.array([[1, 2, 3], [30, 20, 10]]),
sample_ids=sample_mc.to_series().index,
observation_ids=feature_mc.to_series().index)
# Sample x Sum
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(table, result)
# Sample x Mean
result = group(table, axis='sample', metadata=sample_mc,
mode='mean-ceiling')
self.assertEqual(table, result)
# Sample x Median
result = group(table, axis='sample', metadata=sample_mc,
mode='median-ceiling')
self.assertEqual(table, result)
# Feature x Sum
result = group(table, axis='feature', metadata=feature_mc, mode='sum')
self.assertEqual(table, result)
# Feature x Mean
result = group(table, axis='feature', metadata=feature_mc,
mode='mean-ceiling')
self.assertEqual(table, result)
# Feature x Median
result = group(table, axis='feature', metadata=feature_mc,
mode='median-ceiling')
self.assertEqual(table, result)
