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Code Issues Packages Projects Releases Wiki Activity

do not hard-wire column names in annotations (#785)

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* enforce column name uniqueness for obs and var

* parameterize the column name containing obs and var user-readable names

* use the new annotation index value from schema

* update f/e unit tests

* PR review suggestions

* lint
This commit is contained in:
Bruce Martin
2019-05-24 21:00:54 -07:00
committed by GitHub
parent a8c2e408d1
commit 3dc45d6330
16 changed files with 246 additions and 155 deletions
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server/test/test_scanpy_engine.py
+17 -9
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@@ -31,9 +31,11 @@ class EngineTest(unittest.TestCase):
self.assertTrue(self.data.data.X[0, 0] - -0.171_469_51 < epsilon)
def test_mandatory_annotations(self):
self.assertIn("name", self.data.data.obs)
obs_index_col_name = self.data.schema["annotations"]["obs"]["index"]
self.assertIn(obs_index_col_name, self.data.data.obs)
self.assertEqual(list(self.data.data.obs.index), list(range(2638)))
self.assertIn("name", self.data.data.var)
var_index_col_name = self.data.schema["annotations"]["var"]["index"]
self.assertIn(var_index_col_name, self.data.data.var)
self.assertEqual(list(self.data.data.var.index), list(range(1838)))
@pytest.mark.filterwarnings("ignore:Scanpy data matrix")
@@ -70,12 +72,14 @@ class EngineTest(unittest.TestCase):
self.assertEqual(data["n_cols"], 91)
def test_obs_and_var_names(self):
self.assertEqual(np.sum(self.data.data.var["name"].isna()), 0)
self.assertEqual(np.sum(self.data.data.obs["name"].isna()), 0)
self.assertEqual(np.sum(self.data.data.var[self.data.schema["annotations"]["var"]["index"]].isna()), 0)
self.assertEqual(np.sum(self.data.data.obs[self.data.schema["annotations"]["obs"]["index"]].isna()), 0)
def test_schema(self):
with open(path.join(path.dirname(__file__), "schema.json")) as fh:
schema = json.load(fh)
print(schema)
print(self.data.schema)
self.assertEqual(self.data.schema, schema)
def test_schema_produces_error(self):
@@ -108,16 +112,18 @@ class EngineTest(unittest.TestCase):
annotations = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(annotations["n_rows"], 2638)
self.assertEqual(annotations["n_cols"], 5)
obs_index_col_name = self.data.schema["annotations"]["obs"]["index"]
self.assertEqual(
annotations["col_idx"],
["name", "n_genes", "percent_mito", "n_counts", "louvain"],
[obs_index_col_name, "n_genes", "percent_mito", "n_counts", "louvain"],
)
fbs = self.data.annotation_to_fbs_matrix("var")
annotations = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(annotations['n_rows'], 1838)
self.assertEqual(annotations['n_cols'], 2)
self.assertEqual(annotations["col_idx"], ["name", "n_cells"])
var_index_col_name = self.data.schema["annotations"]["var"]["index"]
self.assertEqual(annotations["col_idx"], [var_index_col_name, "n_cells"])
def test_annotation_fields(self):
fbs = self.data.annotation_to_fbs_matrix("obs", ["n_genes", "n_counts"])
@@ -125,7 +131,8 @@ class EngineTest(unittest.TestCase):
self.assertEqual(annotations["n_rows"], 2638)
self.assertEqual(annotations['n_cols'], 2)
fbs = self.data.annotation_to_fbs_matrix("var", ["name"])
var_index_col_name = self.data.schema["annotations"]["var"]["index"]
fbs = self.data.annotation_to_fbs_matrix("var", [var_index_col_name])
annotations = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(annotations['n_rows'], 1838)
self.assertEqual(annotations['n_cols'], 1)
@@ -163,9 +170,10 @@ class EngineTest(unittest.TestCase):
self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
def test_data_named_gene(self):
var_index_col_name = self.data.schema["annotations"]["var"]["index"]
filter_ = {
"filter": {
"var": {"annotation_value": [{"name": "name", "values": ["RER1"]}]}
"var": {"annotation_value": [{"name": var_index_col_name, "values": ["RER1"]}]}
}
}
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
@@ -176,7 +184,7 @@ class EngineTest(unittest.TestCase):
filter_ = {
"filter": {
"var": {"annotation_value": [{"name": "name", "values": ["SPEN", "TYMP", "PRMT2"]}]}
"var": {"annotation_value": [{"name": var_index_col_name, "values": ["SPEN", "TYMP", "PRMT2"]}]}
}
}
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")