def test_filter_table_underscores_escaped(self):
table = pd.DataFrame([[2.0, 2.0], [1.0, 1.0], [9.0, 8.0], [0.0, 4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat1', 'feat2'])
taxonomy = qiime2.Metadata(
pd.DataFrame(['aa; bb; cc', 'aa; bb; dd ee'],
index=pd.Index(['feat1', 'feat2'], name='id'),
columns=['Taxon']))
# keep feat1 only - underscore not treated as a wild card
obs = filter_table(table, taxonomy, include='cc,d_')
exp = pd.DataFrame([[2.0], [1.0], [9.0]],
index=['A', 'B', 'C'],
columns=['feat1'])
pdt.assert_frame_equal(obs, exp, check_like=True)
# keep feat1 only - underscore in query matches underscore in
# taxonomy annotation
taxonomy = qiime2.Metadata(
pd.DataFrame(['aa; bb; c_', 'aa; bb; dd ee'],
index=pd.Index(['feat1', 'feat2'], name='id'),
columns=['Taxon']))
obs = filter_table(table, taxonomy, include='c_')
exp = pd.DataFrame([[2.0], [1.0], [9.0]],
index=['A', 'B', 'C'],
columns=['feat1'])
pdt.assert_frame_equal(obs, exp, check_like=True)
def test_alt_delimiter(self):
table = pd.DataFrame([[2.0, 2.0], [1.0, 1.0], [9.0, 8.0], [0.0, 4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat1', 'feat2'])
taxonomy = qiime2.Metadata(
pd.DataFrame(['aa; bb; cc', 'aa; bb; dd ee'],
index=pd.Index(['feat1', 'feat2'], name='id'),
columns=['Taxon']))
# include with delimiter
obs = filter_table(table,
taxonomy,
include='cc@peanut@ee',
query_delimiter='@peanut@')
pdt.assert_frame_equal(obs, table, check_like=True)
# exclude with delimiter
obs = filter_table(table,
taxonomy,
exclude='ww@peanut@ee',
query_delimiter='@peanut@')
exp = pd.DataFrame([[2.0], [1.0], [9.0]],
index=['A', 'B', 'C'],
columns=['feat1'])
pdt.assert_frame_equal(obs, exp, check_like=True)
def test_missing_taxon_errors(self):
table = pd.DataFrame([[2.0, 2.0], [1.0, 1.0], [9.0, 8.0], [0.0, 4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat1', 'feat2'])
taxonomy = qiime2.Metadata(
pd.DataFrame(['aa; bb; cc'],
index=pd.Index(['feat1'], name='id'),
columns=['Taxon']))
with self.assertRaisesRegex(ValueError, expected_regex='All.*feat2'):
filter_table(table, taxonomy, include='bb')
def test_filter_table_unknown_mode(self):
table = pd.DataFrame([[2.0, 2.0], [1.0, 1.0], [9.0, 8.0], [0.0, 4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat1', 'feat2'])
taxonomy = qiime2.Metadata(
pd.DataFrame(['aa; bb; cc', 'aa; bb; dd ee'],
index=pd.Index(['feat1', 'feat2'], name='id'),
columns=['Taxon']))
with self.assertRaisesRegex(ValueError, 'Unknown mode'):
filter_table(table, taxonomy, include='bb', mode='not-a-mode')
def test_filter_no_filters(self):
table = pd.DataFrame([[2.0, 2.0], [1.0, 1.0], [9.0, 8.0], [0.0, 4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat1', 'feat2'])
taxonomy = qiime2.Metadata(
pd.DataFrame(['aa; bb; cc', 'aa; bb; dd ee'],
index=pd.Index(['feat1', 'feat2'], name='id'),
columns=['Taxon']))
with self.assertRaisesRegex(ValueError, 'At least one'):
filter_table(table, taxonomy)
def test_filter_table_include(self):
table = pd.DataFrame([[2.0, 2.0], [1.0, 1.0], [9.0, 8.0], [0.0, 4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat1', 'feat2'])
taxonomy = qiime2.Metadata(
pd.DataFrame(['aa; bb; cc', 'aa; bb; dd ee'],
index=pd.Index(['feat1', 'feat2'], name='id'),
columns=['Taxon']))
# keep both features
obs = filter_table(table, taxonomy, include='bb')
pdt.assert_frame_equal(obs, table, check_like=True)
obs = filter_table(table, taxonomy, include='cc,ee')
pdt.assert_frame_equal(obs, table, check_like=True)
# keep feat1 only
obs = filter_table(table, taxonomy, include='cc')
exp = pd.DataFrame([[2.0], [1.0], [9.0]],
index=['A', 'B', 'C'],
columns=['feat1'])
pdt.assert_frame_equal(obs, exp, check_like=True)
obs = filter_table(table, taxonomy, include='aa; bb; cc')
exp = pd.DataFrame([[2.0], [1.0], [9.0]],
index=['A', 'B', 'C'],
columns=['feat1'])
pdt.assert_frame_equal(obs, exp, check_like=True)
# keep feat2 only
obs = filter_table(table, taxonomy, include='dd')
exp = pd.DataFrame([[2.0], [1.0], [8.0], [4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat2'])
pdt.assert_frame_equal(obs, exp, check_like=True)
obs = filter_table(table, taxonomy, include='ee')
exp = pd.DataFrame([[2.0], [1.0], [8.0], [4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat2'])
pdt.assert_frame_equal(obs, exp, check_like=True)
obs = filter_table(table, taxonomy, include='dd ee')
exp = pd.DataFrame([[2.0], [1.0], [8.0], [4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat2'])
pdt.assert_frame_equal(obs, exp, check_like=True)
obs = filter_table(table, taxonomy, include='aa; bb; dd ee')
exp = pd.DataFrame([[2.0], [1.0], [8.0], [4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat2'])
pdt.assert_frame_equal(obs, exp, check_like=True)
# keep no features
with self.assertRaisesRegex(ValueError, expected_regex='empty table'):
obs = filter_table(table, taxonomy, include='peanut!')
def test_all_features_with_frequency_greater_than_zero_get_filtered(self):
table = pd.DataFrame([[2.0, 0.0], [1.0, 0.0], [9.0, 0.0], [1.0, 0.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat1', 'feat2'])
taxonomy = qiime2.Metadata(
pd.DataFrame(['aa; bb; cc', 'aa; bb; dd ee'],
index=pd.Index(['feat1', 'feat2'], name='id'),
columns=['Taxon']))
# empty - feat2, which is matched by the include term, has a frequency
# of zero in all samples, so all samples end up dropped from the table
with self.assertRaisesRegex(ValueError,
expected_regex='greater than zero'):
filter_table(table, taxonomy, include='dd')
def test_extra_taxon_ignored(self):
table = pd.DataFrame([[2.0, 2.0], [1.0, 1.0], [9.0, 8.0], [0.0, 4.0]],
index=['A', 'B', 'C', 'D'],
columns=['feat1', 'feat2'])
taxonomy = qiime2.Metadata(
pd.DataFrame(['aa; bb; cc', 'aa; bb; dd ee', 'aa; bb; cc'],
index=pd.Index(['feat1', 'feat2', 'feat3'],
name='id'),
columns=['Taxon']))
# keep both features
obs = filter_table(table, taxonomy, include='bb')
pdt.assert_frame_equal(obs, table, check_like=True)
