def test_combine_frequency_filters(self):
# no filtering
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=1, max_frequency=5)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=2, max_frequency=2)
expected = Table(np.array([[1], [1]]),
['O1', 'O2'],
['S2'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=2, min_features=2)
expected = Table(np.array([[1], [1]]),
['O1', 'O2'],
['S2'])
self.assertEqual(actual, expected)
def test_combine_id_and_frequency_filters(self):
# no filtering
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' OR Subject='subject-2'"
actual = filter_samples(table, metadata=metadata, where=where,
min_frequency=1)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# id and frequency filters active
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1'"
actual = filter_samples(table, metadata=metadata, where=where,
min_frequency=2)
expected = Table(np.array([[1], [1]]),
['O1', 'O2'],
['S2'])
self.assertEqual(actual, expected)
def test_min_frequency(self):
# no filtering
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=1)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=2)
expected = Table(np.array([[1, 3], [1, 2]]), ['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=3)
expected = Table(np.array([[3], [2]]), ['O1', 'O2'], ['S3'])
self.assertEqual(actual, expected)
# filter all
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=42)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_max_frequency(self):
# no filtering
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=42)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=4)
expected = Table(np.array([[0, 1], [1, 1]]), ['O1', 'O2'],
['S1', 'S2'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=1)
expected = Table(np.array([[1]]), ['O2'], ['S1'])
self.assertEqual(actual, expected)
# filter all
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=0)
self.assertTrue(actual.is_empty())
def test_max_features(self):
# no filtering
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_features=2)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_features=1)
expected = Table(np.array([[1]]),
['O2'],
['S1'])
self.assertEqual(actual, expected)
# filter all
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_features=0)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_max_features(self):
# no filtering
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_features=2)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_features=1)
expected = Table(np.array([[1]]),
['O2'],
['S1'])
self.assertEqual(actual, expected)
# filter all
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_features=0)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_combine_id_and_frequency_filters(self):
# no filtering
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' OR Subject='subject-2'"
actual = filter_samples(table, sample_metadata=metadata, where=where,
min_frequency=1)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# id and frequency filters active
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1'"
actual = filter_samples(table, sample_metadata=metadata, where=where,
min_frequency=2)
expected = Table(np.array([[1], [1]]),
['O1', 'O2'],
['S2'])
self.assertEqual(actual, expected)
def test_combine_frequency_filters(self):
# no filtering
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=1, max_frequency=5)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=2, max_frequency=2)
expected = Table(np.array([[1], [1]]),
['O1', 'O2'],
['S2'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=2, min_features=2)
expected = Table(np.array([[1], [1]]),
['O1', 'O2'],
['S2'])
self.assertEqual(actual, expected)
def test_where(self):
# no filtering
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' OR Subject='subject-2'"
actual = filter_samples(table, sample_metadata=metadata, where=where)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1'"
actual = filter_samples(table, sample_metadata=metadata, where=where)
expected = Table(np.array([[0, 1], [1, 1]]),
['O1', 'O2'],
['S1', 'S2'])
self.assertEqual(actual, expected)
# filter two
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' AND SampleType='gut'"
actual = filter_samples(table, sample_metadata=metadata, where=where)
expected = Table(np.array([[1]]),
['O2'],
['S1'])
self.assertEqual(actual, expected)
# filter all
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' AND Subject='subject-2'"
actual = filter_samples(table, sample_metadata=metadata, where=where)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_invalid_args(self):
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
with self.assertRaises(ValueError):
filter_samples(table)
with self.assertRaises(ValueError):
filter_samples(table, where="Subject='subject-1'")
def test_filter_empty_features(self):
# no filtering
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=42,
filter_empty_features=False)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=4,
filter_empty_features=False)
expected = Table(np.array([[0, 1], [1, 1]]),
['O1', 'O2'],
['S1', 'S2'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=1,
filter_empty_features=False)
expected = Table(np.array([[0], [1]]),
['O1', 'O2'],
['S1'])
self.assertEqual(actual, expected)
# filter all
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=0,
filter_empty_features=False)
expected = Table(np.array([[], []]), ['O1', 'O2'], [])
self.assertEqual(actual, expected)
def test_sample_metadata_extra_ids(self):
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=['S-not-in-table', 'S2', 'S3'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, sample_metadata=metadata)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
def test_min_features(self):
# no filtering
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_features=1)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_features=2)
expected = Table(np.array([[1, 3], [1, 2]]), ['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# filter all
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_features=3)
self.assertTrue(actual.is_empty())
def test_min_frequency(self):
# no filtering
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=1)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=2)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=3)
expected = Table(np.array([[3], [2]]),
['O1', 'O2'],
['S3'])
self.assertEqual(actual, expected)
# filter all
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, min_frequency=42)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_sample_metadata(self):
# no filtering
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=['S1', 'S2', 'S3'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, sample_metadata=metadata)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
df = pd.DataFrame({'Subject': ['subject-1', 'subject-2'],
'SampleType': ['tongue', 'gut']},
index=['S2', 'S3'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, sample_metadata=metadata)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# filter all
df = pd.DataFrame({})
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, sample_metadata=metadata)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_invalid_args(self):
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
with self.assertRaisesRegex(ValueError, "No filtering"):
filter_samples(table)
with self.assertRaisesRegex(ValueError, "'where' is specified."):
filter_samples(table, where="Subject='subject-1'")
with self.assertRaisesRegex(ValueError, "'exclude_ids' is True."):
filter_samples(table, exclude_ids=True)
def test_invalid_args(self):
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
with self.assertRaisesRegex(ValueError, "No filtering"):
filter_samples(table)
with self.assertRaisesRegex(ValueError,
"'where' is specified."):
filter_samples(table, where="Subject='subject-1'")
with self.assertRaisesRegex(ValueError,
"'exclude_ids' is True."):
filter_samples(table, exclude_ids=True)
def test_combine_exclude_ids_and_features_filters(self):
# exclude one, min_features filter none
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S1'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_features=2)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude one, min_features filter one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_features=2)
expected = Table(np.array([[3], [2]]),
['O1', 'O2'],
['S3'])
self.assertEqual(actual, expected)
# exclude one, min_features filter for same one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[4, 1, 3], [6, 0, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_features=1)
expected = Table(np.array([[4, 3], [6, 2]]),
['O1', 'O2'],
['S1', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max_features filter none
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S1'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_features=3)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max_features filter one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_features=1)
expected = Table(np.array([[1]]), ['O2'], ['S1'])
self.assertEqual(actual, expected)
# exclude one, max_features filter for same one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 10, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_features=9)
expected = Table(np.array([[0, 3], [1, 2]]),
['O1', 'O2'],
['S1', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max_features filter none,
# min_features filter none
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [0, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_features=0,
max_features=5)
expected = Table(np.array([[0, 3], [0, 2]]),
['O1', 'O2'],
['S1', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max_features filter one,
# min_features filter one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [0, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_features=1,
max_features=1)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
# where filter one -> exclude one,
# max_features filter one,
# min_features filter one
df = pd.DataFrame({'Subject': ['subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue']},
index=pd.Index(['S1', 'S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [0, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "SampleType='tongue'"
actual = filter_samples(table,
metadata=metadata,
where=where,
exclude_ids=True,
min_features=1,
max_features=1)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_where(self):
# no filtering
df = pd.DataFrame(
{
'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']
},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' OR Subject='subject-2'"
actual = filter_samples(table, metadata=metadata, where=where)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
df = pd.DataFrame(
{
'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']
},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1'"
actual = filter_samples(table, metadata=metadata, where=where)
expected = Table(np.array([[0, 1], [1, 1]]), ['O1', 'O2'],
['S1', 'S2'])
self.assertEqual(actual, expected)
# filter two
df = pd.DataFrame(
{
'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']
},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' AND SampleType='gut'"
actual = filter_samples(table, metadata=metadata, where=where)
expected = Table(np.array([[1]]), ['O2'], ['S1'])
self.assertEqual(actual, expected)
# filter all
df = pd.DataFrame(
{
'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']
},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' AND Subject='subject-2'"
actual = filter_samples(table, metadata=metadata, where=where)
self.assertTrue(actual.is_empty())
# filter none -> exclude none
df = pd.DataFrame(
{
'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']
},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' AND SampleType='elbow'"
actual = filter_samples(table,
metadata=metadata,
where=where,
exclude_ids=True)
self.assertEqual(actual, table)
# filter one -> exclude one
df = pd.DataFrame(
{
'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']
},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' AND SampleType='gut'"
actual = filter_samples(table,
metadata=metadata,
where=where,
exclude_ids=True)
expected = Table(np.array([[1, 3], [1, 2]]), ['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# filter two -> exclude two
df = pd.DataFrame(
{
'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']
},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1'"
actual = filter_samples(table,
metadata=metadata,
where=where,
exclude_ids=True)
expected = Table(np.array([[3], [2]]), ['O1', 'O2'], ['S3'])
self.assertEqual(actual, expected)
# filter all -> exclude all
df = pd.DataFrame(
{
'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']
},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' OR Subject='subject-2'"
actual = filter_samples(table,
metadata=metadata,
where=where,
exclude_ids=True)
self.assertTrue(actual.is_empty())
def test_sample_metadata(self):
# no filtering
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
df = pd.DataFrame({'Subject': ['subject-1', 'subject-2'],
'SampleType': ['tongue', 'gut']},
index=pd.Index(['S2', 'S3'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# filter all
df = pd.DataFrame({}, index=pd.Index(['foo'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
# exclude none
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S90'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata, exclude_ids=True)
self.assertEqual(actual, table)
# exclude one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S1'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata, exclude_ids=True)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude two
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1'],
'SampleType': ['gut', 'tongue']},
index=pd.Index(['S1', 'S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata, exclude_ids=True)
expected = Table(np.array([[3], [2]]),
['O1', 'O2'],
['S3'])
self.assertEqual(actual, expected)
# exclude all
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='id'))
metadata = qiime2.Metadata(df)
actual = filter_samples(table, metadata=metadata,
exclude_ids=True)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_combine_exclude_ids_and_frequency_filters(self):
# exclude one, min_frequency filter none
df = pd.DataFrame({
'Subject': ['subject-1'],
'SampleType': ['gut']
},
index=['S1'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_frequency=2)
expected = Table(np.array([[1, 3], [1, 2]]), ['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude one, min_frequency filter one
df = pd.DataFrame({
'Subject': ['subject-1'],
'SampleType': ['gut']
},
index=['S1'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_frequency=3)
expected = Table(np.array([[3], [2]]), ['O1', 'O2'], ['S3'])
self.assertEqual(actual, expected)
# exclude one, min_frequency filter for same one
df = pd.DataFrame({
'Subject': ['subject-1'],
'SampleType': ['gut']
},
index=['S1'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [0, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_frequency=1)
expected = Table(np.array([[1, 3], [1, 2]]), ['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max_frequency filter none
df = pd.DataFrame({
'Subject': ['subject-1'],
'SampleType': ['gut']
},
index=['S1'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_frequency=42)
expected = Table(np.array([[1, 3], [1, 2]]), ['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max_frequency filter one
df = pd.DataFrame({
'Subject': ['subject-1'],
'SampleType': ['gut']
},
index=['S1'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_frequency=4)
expected = Table(np.array([[1], [1]]), ['O1', 'O2'], ['S2'])
self.assertEqual(actual, expected)
# exclude one, max_frequency filter for same one
df = pd.DataFrame({
'Subject': ['subject-1'],
'SampleType': ['gut']
},
index=['S1'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[5, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_frequency=5)
expected = Table(np.array([[1, 3], [1, 2]]), ['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max & min_frequency filter none
df = pd.DataFrame({
'Subject': ['subject-1'],
'SampleType': ['gut']
},
index=['S1'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_frequency=42,
min_frequency=0)
expected = Table(np.array([[1, 3], [1, 2]]), ['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude one, min_frequency filter one,
# max_frequency filter one
df = pd.DataFrame({
'Subject': ['subject-1'],
'SampleType': ['gut']
},
index=['S1'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_frequency=4,
min_frequency=3)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
# where filter one -> exclude one,
# min_frequency filter one,
# max_frequency filter one
df = pd.DataFrame(
{
'Subject': ['subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue']
},
index=['S1', 'S2'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1'"
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
where=where,
max_frequency=4,
min_frequency=3)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_combine_exclude_ids_and_features_filters(self):
# exclude one, min_features filter none
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S1'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_features=2)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude one, min_features filter one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_features=2)
expected = Table(np.array([[3], [2]]),
['O1', 'O2'],
['S3'])
self.assertEqual(actual, expected)
# exclude one, min_features filter for same one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[4, 1, 3], [6, 0, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_features=1)
expected = Table(np.array([[4, 3], [6, 2]]),
['O1', 'O2'],
['S1', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max_features filter none
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S1'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_features=3)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max_features filter one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_features=1)
expected = Table(np.array([[1]]), ['O2'], ['S1'])
self.assertEqual(actual, expected)
# exclude one, max_features filter for same one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 10, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
max_features=9)
expected = Table(np.array([[0, 3], [1, 2]]),
['O1', 'O2'],
['S1', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max_features filter none,
# min_features filter none
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [0, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_features=0,
max_features=5)
expected = Table(np.array([[0, 3], [0, 2]]),
['O1', 'O2'],
['S1', 'S3'])
self.assertEqual(actual, expected)
# exclude one, max_features filter one,
# min_features filter one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [0, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table,
metadata=metadata,
exclude_ids=True,
min_features=1,
max_features=1)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
# where filter one -> exclude one,
# max_features filter one,
# min_features filter one
df = pd.DataFrame({'Subject': ['subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue']},
index=pd.Index(['S1', 'S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [0, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "SampleType='tongue'"
actual = filter_samples(table,
metadata=metadata,
where=where,
exclude_ids=True,
min_features=1,
max_features=1)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_sample_metadata(self):
# no filtering
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
df = pd.DataFrame({'Subject': ['subject-1', 'subject-2'],
'SampleType': ['tongue', 'gut']},
index=pd.Index(['S2', 'S3'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# filter all
df = pd.DataFrame({}, index=pd.Index(['foo'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
# exclude none
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S90'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata, exclude_ids=True)
self.assertEqual(actual, table)
# exclude one
df = pd.DataFrame({'Subject': ['subject-1'],
'SampleType': ['gut']},
index=pd.Index(['S1'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata, exclude_ids=True)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
# exclude two
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1'],
'SampleType': ['gut', 'tongue']},
index=pd.Index(['S1', 'S2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, metadata=metadata, exclude_ids=True)
expected = Table(np.array([[3], [2]]),
['O1', 'O2'],
['S3'])
self.assertEqual(actual, expected)
# exclude all
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='id'))
metadata = qiime2.Metadata(df)
actual = filter_samples(table, metadata=metadata,
exclude_ids=True)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
