style: black + ruff formatting

Author: kaloster

scripts/cxg_to_zarr.py

@@ -37,9 +37,7 @@ def _coerce(values, col_schema):
         arr = np.asarray(values)
         if arr.dtype.kind in ("i", "u"):
             return pd.Categorical.from_codes(arr, categories=categories)
-        return pd.Categorical(
-            [v.decode() if isinstance(v, bytes) else str(v) for v in arr], categories=categories
-        )
+        return pd.Categorical([v.decode() if isinstance(v, bytes) else str(v) for v in arr], categories=categories)
     s = pd.Series(values)
     if pd.api.types.is_categorical_dtype(s):
         return s.values

server/dataset/zarr_dataset.py

@@ -117,7 +117,7 @@ def _obsm_key(self, ename):
         return ename if ename in self.adata.obsm else f"X_{ename}"
 
     def get_embedding_names(self):
-        names = [k[2:] if k.startswith("X_") else k for k in self.adata.obsm.keys()]
+        names = [k[2:] if k.startswith("X_") else k for k in self.adata.obsm]
         if not names:
             raise DatasetAccessError("zarr matrix missing embeddings")
         return names
@@ -169,9 +169,14 @@ def mask(rows):
         meanA, varA, nA = mean_var_n(self.get_X_array(mask(rowsA)), dist)
         meanB, varB, nB = mean_var_n(self.get_X_array(mask(rowsB)), dist)
         return diffexp_ttest_from_mean_var(
-            meanA=meanA.astype(dtype), varA=varA.astype(dtype), nA=nA,
-            meanB=meanB.astype(dtype), varB=varB.astype(dtype), nB=nB,
-            top_n=top_n, diffexp_lfc_cutoff=lfc_cutoff,
+            meanA=meanA.astype(dtype),
+            varA=varA.astype(dtype),
+            nA=nA,
+            meanB=meanB.astype(dtype),
+            varB=varB.astype(dtype),
+            nB=nB,
+            top_n=top_n,
+            diffexp_lfc_cutoff=lfc_cutoff,
         )
 
     # ---- metadata: colors, uns, genesets ----------------------------------

server/tests/unit/dataset/test_zarr_dataset.py

@@ -50,9 +50,7 @@ def test_schema_structure(self):
             {c["name"] for c in sz["annotations"]["obs"]["columns"]},
             {c["name"] for c in sc["annotations"]["obs"]["columns"]},
         )
-        self.assertEqual(
-            sz["annotations"]["obs"]["index"], sc["annotations"]["obs"]["index"]
-        )
+        self.assertEqual(sz["annotations"]["obs"]["index"], sc["annotations"]["obs"]["index"])
 
     def test_x_slice(self):
         var_mask = np.zeros(self.cxg.get_shape()[1], dtype=bool)
@@ -89,12 +87,8 @@ def test_diffexp_parity(self):
         rz = self.zarr.compute_diffexp_ttest(maskA, maskB, top_n=10)
         rc = self.cxg.compute_diffexp_ttest(maskA, maskB, top_n=10)
         # same top gene indices
-        self.assertEqual(
-            [r[0] for r in rz["positive"]], [r[0] for r in rc["positive"]]
-        )
-        np.testing.assert_allclose(
-            [r[1] for r in rz["positive"]], [r[1] for r in rc["positive"]], rtol=1e-4
-        )
+        self.assertEqual([r[0] for r in rz["positive"]], [r[0] for r in rc["positive"]])
+        np.testing.assert_allclose([r[1] for r in rz["positive"]], [r[1] for r in rc["positive"]], rtol=1e-4)
 
 
 if __name__ == "__main__":