def test_group_taxa_data_table_alt(self):
with patch.object(TaxonomyModel, 'presence_data_table') as mock_model:
mock_model.return_value = DataTable.from_dataframe(
pd.DataFrame({
'sampleId': ['sample-1', 'sample-1', 'sample-2'],
'rank_1': ['a', 'a', 'a'],
'rank_2': ['b', 'f', 'b'],
}))
response, code = group_taxa_present_alt(
{'sample_ids': ['sample-1', 'sample-2']}, "dataset1",
"some-table")
self.assertEqual(code, 200)
exp_keys = ['data', 'columns']
obs = json.loads(response)
self.assertCountEqual(exp_keys, obs.keys())
self.assertCountEqual(obs['columns'], [{
'data': 'sampleId'
}, {
'data': 'rank_1'
}, {
'data': 'rank_2'
}])
for item in obs['data']:
self.assertCountEqual(item.keys(),
['sampleId', 'rank_1', 'rank_2'])
def presence_data_table(self, ids: Iterable[str]) -> DataTable:
table = self._table.filter(set(ids), inplace=False).remove_empty()
features = table.ids(axis='observation')
entries = list()
for vec, sample_id, _ in table.iter(dense=False):
for feature_idx, val in zip(vec.indices, vec.data):
entry = {
'sampleId': sample_id,
'relativeAbundance': val,
**self._formatted_taxa_names[features[feature_idx]],
}
entries.append(entry)
sample_data = pd.DataFrame(
entries,
# this enforces the column order
columns=['sampleId'] + self._formatter.labels +
['relativeAbundance'],
# need the .astype('object') in case a
# column is completely empty (filled with
# Nan, default dtype is numeric,
# which cannot be replaced with None.
# Need None because it is valid for JSON,
# but NaN is not.
).astype('object')
sample_data[pd.isna(sample_data)] = None
return DataTable.from_dataframe(sample_data)
def test_data_table_from_dataframe(self):
dict_ = {
'data': [{
'foo': 'baz',
'bar': 'qux'
}, {
'foo': 'quuz',
'bar': 'corge'
}],
'columns': ['foo', 'bar'],
}
df = pd.DataFrame(dict_['data'], columns=dict_['columns'])
dt = DataTable.from_dataframe(df)
dict_['columns'] = [{'data': 'foo'}, {'data': 'bar'}]
obs_dict = dt.to_dict()
obs = json.dumps(obs_dict)
exp = json.dumps(dict_)
self.assertEqual(obs, exp)
def test_group_taxa_data_table(self):
with patch('microsetta_public_api.repo._taxonomy_repo.TaxonomyRepo.'
'tables', new_callable=PropertyMock) as mock_tables, \
patch.object(TaxonomyModel, 'presence_data_table') as \
mock_model:
mock_tables.return_value = {
'some-table': {
'table': self.table,
'feature-data-taxonomy': self.taxonomy_df,
'variances': self.table_vars,
'model': self.taxonomy_model,
},
}
mock_model.return_value = DataTable.from_dataframe(
pd.DataFrame({
'sampleId': ['sample-1', 'sample-1', 'sample-2'],
'rank_1': ['a', 'a', 'a'],
'rank_2': ['b', 'f', 'b'],
}))
response, code = group_taxa_present(
{'sample_ids': ['sample-1', 'sample-2']}, "some-table")
self.assertEqual(code, 200)
exp_keys = ['data', 'columns']
obs = json.loads(response)
self.assertCountEqual(exp_keys, obs.keys())
self.assertCountEqual(obs['columns'], [{
'data': 'sampleId'
}, {
'data': 'rank_1'
}, {
'data': 'rank_2'
}])
for item in obs['data']:
self.assertCountEqual(item.keys(),
['sampleId', 'rank_1', 'rank_2'])