add update

src/rapids_singlecell/preprocessing/_normalize.py

@@ -88,7 +88,7 @@ def _normalize_total(X: ArrayTypesDask, target_sum: int):
         return _normalize_total_csr(X, target_sum)
     elif isinstance(X, DaskArray):
         return _normalize_total_dask(X, target_sum)
-    else:
+    elif isinstance(X, cp.ndarray):
         from ._kernels._norm_kernel import _mul_dense
 
         if not X.flags.c_contiguous:
@@ -100,6 +100,8 @@ def _normalize_total(X: ArrayTypesDask, target_sum: int):
             (X, X.shape[0], X.shape[1], int(target_sum)),
         )
         return X
+    else:
+        raise ValueError(f"Cannot normalize {type(X)}")
 
 
 def _normalize_total_csr(X: sparse.csr_matrix, target_sum: int) -> sparse.csr_matrix:

src/rapids_singlecell/preprocessing/_sparse_pca/_sparse_pca.py

@@ -1,3 +1,5 @@
+# This file will be removed in Q3 2025 when in favor of the CUML implementation
+
 from __future__ import annotations
 
 import math

src/rapids_singlecell/preprocessing/_utils.py

@@ -231,6 +231,8 @@ def _get_mean_var(X, axis=0):
                 major = X.shape[1]
                 minor = X.shape[0]
                 mean, var = _mean_var_minor(X, major, minor)
+        else:
+            raise ValueError("axis must be either 0 or 1")
     elif isinstance(X, DaskArray):
         if isspmatrix_csr(X._meta):
             if axis == 0:
@@ -243,6 +245,10 @@ def _get_mean_var(X, axis=0):
                 mean, var = _mean_var_major_dask(X, major, minor)
         elif isinstance(X._meta, cp.ndarray):
             mean, var = _mean_var_dense_dask(X, axis)
+        else:
+            raise ValueError(
+                "Type not supported. Please provide a CuPy ndarray or a CuPy sparse matrix. Or a Dask array with a CuPy ndarray or a CuPy sparse matrix as meta."
+            )
     else:
         mean, var = _mean_var_dense(X, axis)
     return mean, var