class EngineTest(unittest.TestCase):
def setUp(self):
args = {
"layout": ["umap"],
"max_category_items": 100,
"obs_names": None,
"var_names": None,
"diffexp_lfc_cutoff": 0.01,
"layout_file": None,
"backed": self.backed
}
self.data = ScanpyEngine(DataLocator(self.data_locator), args)
def test_init(self):
self.assertEqual(self.data.cell_count, 2638)
self.assertEqual(self.data.gene_count, 1838)
epsilon = 0.000_005
self.assertTrue(self.data.data.X[0, 0] - -0.171_469_51 < epsilon)
def test_mandatory_annotations(self):
obs_index_col_name = self.data.get_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)))
var_index_col_name = self.data.get_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")
def test_data_type(self):
# don't run the test on the more exotic data types, as they don't
# support the astype() interface (used by this test, but not underlying app)
if isinstance(self.data.data.X, np.ndarray):
self.data.data.X = self.data.data.X.astype("float64")
with self.assertWarns(UserWarning):
self.data._validate_data_types()
def test_filter_idx(self):
filter_ = {"filter": {"var": {"index": [1, 99, [200, 300]]}}}
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
data = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(data["n_rows"], 2638)
self.assertEqual(data["n_cols"], 102)
def test_filter_complex(self):
filter_ = {
"filter": {
"var": {
"annotation_value": [{
"name": "n_cells",
"min": 10
}],
"index": [1, 99, [200, 300]]
}
}
}
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
data = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(data["n_rows"], 2638)
self.assertEqual(data["n_cols"], 91)
def test_obs_and_var_names(self):
self.assertEqual(
np.sum(self.data.data.var[self.data.get_schema()["annotations"]
["var"]["index"]].isna()), 0)
self.assertEqual(
np.sum(self.data.data.obs[self.data.get_schema()["annotations"]
["obs"]["index"]].isna()), 0)
def test_get_schema(self):
with open(path.join(path.dirname(__file__), "schema.json")) as fh:
schema = json.load(fh)
self.assertEqual(self.data.get_schema(), schema)
def test_schema_produces_error(self):
self.data.data.obs["time"] = pd.Series(
list([time.time() for i in range(self.data.cell_count)]),
dtype="datetime64[ns]",
)
with pytest.raises(TypeError):
self.data._create_schema()
def test_config(self):
self.assertEqual(
self.data.features["layout"]["obs"],
{
"available": True,
"interactiveLimit": 50000
},
)
def test_layout(self):
fbs = self.data.layout_to_fbs_matrix()
layout = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(layout["n_cols"], 2)
self.assertEqual(layout["n_rows"], 2638)
X = layout["columns"][0]
self.assertTrue((X >= 0).all() and (X <= 1).all())
Y = layout["columns"][1]
self.assertTrue((Y >= 0).all() and (Y <= 1).all())
def test_annotations(self):
fbs = self.data.annotation_to_fbs_matrix("obs")
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.get_schema(
)["annotations"]["obs"]["index"]
self.assertEqual(
annotations["col_idx"],
[
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)
var_index_col_name = self.data.get_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"])
annotations = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(annotations["n_rows"], 2638)
self.assertEqual(annotations['n_cols'], 2)
var_index_col_name = self.data.get_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)
def test_annotation_put(self):
with self.assertRaises(DisabledFeatureError):
self.data.annotation_put_fbs(None, "obs")
def test_diffexp_topN(self):
f1 = {"filter": {"obs": {"index": [[0, 500]]}}}
f2 = {"filter": {"obs": {"index": [[500, 1000]]}}}
result = json.loads(self.data.diffexp_topN(f1["filter"], f2["filter"]))
self.assertEqual(len(result), 10)
result = json.loads(
self.data.diffexp_topN(f1["filter"], f2["filter"], 20))
self.assertEqual(len(result), 20)
def test_data_frame(self):
f1 = {"var": {"index": [[0, 10]]}}
fbs = self.data.data_frame_to_fbs_matrix(f1, "var")
data = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(data["n_rows"], 2638)
self.assertEqual(data["n_cols"], 10)
with self.assertRaises(ValueError):
self.data.data_frame_to_fbs_matrix(None, "obs")
def test_filtered_data_frame(self):
filter_ = {
"filter": {
"var": {
"annotation_value": [{
"name": "n_cells",
"min": 100
}]
}
}
}
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
data = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(data["n_rows"], 2638)
self.assertEqual(data["n_cols"], 1040)
filter_ = {
"filter": {
"obs": {
"annotation_value": [{
"name": "n_counts",
"min": 3000
}]
}
}
}
with self.assertRaises(FilterError):
self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
def test_data_named_gene(self):
var_index_col_name = self.data.get_schema(
)["annotations"]["var"]["index"]
filter_ = {
"filter": {
"var": {
"annotation_value": [{
"name": var_index_col_name,
"values": ["RER1"]
}]
}
}
}
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
data = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(data["n_rows"], 2638)
self.assertEqual(data["n_cols"], 1)
self.assertEqual(data["col_idx"], [4])
filter_ = {
"filter": {
"var": {
"annotation_value": [{
"name": var_index_col_name,
"values": ["SPEN", "TYMP", "PRMT2"]
}]
}
}
}
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
data = decode_fbs.decode_matrix_FBS(fbs)
self.assertEqual(data["n_rows"], 2638)
self.assertEqual(data["n_cols"], 3)
self.assertTrue((data["col_idx"] == [15, 1818, 1837]).all())
class NaNTest(unittest.TestCase):
def setUp(self):
self.args = {
"layout": "umap",
"diffexp": "ttest",
"max_category_items": 100,
"obs_names": None,
"var_names": None,
"diffexp_lfc_cutoff": 0.01,
}
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
self.data = ScanpyEngine("server/test/test_datasets/nan.h5ad",
self.args)
self.data._create_schema()
def test_load(self):
with self.assertWarns(UserWarning):
ScanpyEngine("server/test/test_datasets/nan.h5ad", self.args)
def test_init(self):
self.assertEqual(self.data.cell_count, 100)
self.assertEqual(self.data.gene_count, 100)
epsilon = 0.000_005
self.assertTrue(self.data.data.X[0, 0] - -0.171_469_51 < epsilon)
def test_dataframe(self):
data_frame_var = decode_fbs.decode_matrix_FBS(
self.data.data_frame_to_fbs_matrix(None, "var"))
self.assertIsNotNone(data_frame_var)
self.assertEqual(data_frame_var["n_rows"], 100)
self.assertEqual(data_frame_var["n_cols"], 100)
self.assertTrue(math.isnan(data_frame_var["columns"][3][3]))
with pytest.raises(JSONEncodingValueError):
json.loads(self.data.data_frame(None, "obs"))
with pytest.raises(JSONEncodingValueError):
json.loads(self.data.data_frame(None, "var"))
def test_dataframe_obs_not_implemented(self):
with self.assertRaises(ValueError) as cm:
decode_fbs.decode_matrix_FBS(
self.data.data_frame_to_fbs_matrix(None, "obs"))
self.assertIsNotNone(cm.exception)
def test_annotation(self):
annotations = decode_fbs.decode_matrix_FBS(
self.data.annotation_to_fbs_matrix("obs"))
self.assertEqual(
annotations["col_idx"],
["name", "n_genes", "percent_mito", "n_counts", "louvain"])
self.assertEqual(annotations["n_rows"], 100)
self.assertTrue(math.isnan(annotations["columns"][2][0]))
annotations = decode_fbs.decode_matrix_FBS(
self.data.annotation_to_fbs_matrix("var"))
self.assertEqual(annotations["col_idx"],
["name", "n_cells", "var_with_nans"])
self.assertEqual(annotations["n_rows"], 100)
self.assertTrue(math.isnan(annotations["columns"][2][0]))
with pytest.raises(JSONEncodingValueError):
json.loads(self.data.annotation(None, "obs"))
with pytest.raises(JSONEncodingValueError):
json.loads(self.data.annotation(None, "var"))