Pa/name-conventions

benchmarks/benchmarks/backed_hdf5.py

@@ -13,18 +13,18 @@ class BackedHDF5Indexing:
     param_names = ("arr_type",)
     params = ("sparse",)
 
-    def setup_cache(self):
-        X_sparse = sparse.random(
+    def setup_cache(self) -> None:
+        x_sparse = sparse.random(
             10000,
             50000,
             density=0.01,
             format="csr",
             random_state=np.random.default_rng(42),
         )
-        for X, arr_type in [
-            (X_sparse, "sparse"),
+        for x, arr_type in [
+            (x_sparse, "sparse"),
         ]:
-            n_obs, n_var = X.shape
+            n_obs, n_var = x.shape
 
             # Create obs and var dataframes
             obs = pd.DataFrame(
@@ -45,7 +45,7 @@ def setup_cache(self):
             )
 
             # Create AnnData object and save to HDF5
-            adata = ad.AnnData(X=X, obs=obs, var=var)
+            adata = ad.AnnData(X=x, obs=obs, var=var)
 
             # Create temporary file
             adata.write_h5ad(file_paths[arr_type])

benchmarks/benchmarks/sparse_dataset.py

@@ -41,16 +41,16 @@ class SparseCSRContiguousSlice:
         "use_dask",
     )
 
-    def setup_cache(self):
-        X = sparse.random(
+    def setup_cache(self) -> None:
+        x = sparse.random(
             10_000,
             10_000,
             density=0.01,
             format="csr",
             random_state=np.random.default_rng(42),
         )
         g = zarr.group(self.filepath)
-        write_elem(g, "X", X)
+        write_elem(g, "X", x)
 
     def setup(self, index: str, use_dask: bool):  # noqa: FBT001
         g = zarr.open(self.filepath)
@@ -82,16 +82,16 @@ def peakmem_getitem_adata(self, *_):
 class SparseCSRDask:
     filepath = "data.zarr"
 
-    def setup_cache(self):
-        X = sparse.random(
+    def setup_cache(self) -> None:
+        x = sparse.random(
             10_000,
             10_000,
             density=0.01,
             format="csr",
             random_state=np.random.default_rng(42),
         )
         g = zarr.group(self.filepath)
-        write_elem(g, "X", X)
+        write_elem(g, "X", x)
 
     def setup(self):
         self.group = zarr.group(self.filepath)

benchmarks/benchmarks/utils.py

@@ -95,32 +95,32 @@ def gen_indexer(adata, dim, index_kind, ratio):
 
 def gen_adata(n_obs, n_var, attr_set):
     if "X-csr" in attr_set:
-        X = sparse.random(
+        x = sparse.random(
             n_obs,
             n_var,
             density=0.1,
             format="csr",
             random_state=np.random.default_rng(42),
         )
     elif "X-dense" in attr_set:
-        X = sparse.random(
+        x = sparse.random(
             n_obs,
             n_var,
             density=0.1,
             format="csr",
             random_state=np.random.default_rng(42),
         )
-        X = X.toarray()
+        x = x.toarray()
     else:
         # TODO: There's probably a better way to do this
-        X = sparse.random(
+        x = sparse.random(
             n_obs,
             n_var,
             density=0,
             format="csr",
             random_state=np.random.default_rng(42),
         )
-    adata = AnnData(X)
+    adata = AnnData(x)
     if "obs,var" in attr_set:
         adata.obs = pd.DataFrame(
             {k: np.random.randint(0, 100, n_obs) for k in ascii_lowercase},

pyproject.toml

@@ -198,6 +198,7 @@ select = [
     "I",    # isort
     "ICN",  # Follow import conventions
     "ISC",  # Implicit string concatenation
+    "N",    # Naming conventions
     "PERF", # Performance
     "PIE",  # Syntax simplifications
     "PTH",  # Pathlib instead of os.path