make qc smaller

Author: Intron7

src/rapids_singlecell/preprocessing/_qc.py

@@ -127,32 +127,22 @@ def _basic_qc(
         from rapids_singlecell._cuda import _qc_cuda as _qc
 
         if sparse.isspmatrix_csr(X):
-            _qc.sparse_qc_csr(
-                X.indptr.data.ptr,
-                X.indices.data.ptr,
-                X.data.data.ptr,
-                sums_cells.data.ptr,
-                sums_genes.data.ptr,
-                genes_per_cell.data.ptr,
-                cells_per_gene.data.ptr,
-                int(X.shape[0]),
-                int(cp.dtype(X.data.dtype).itemsize),
-            )
+            sparse_qc = _qc.sparse_qc_csr
         elif sparse.isspmatrix_csc(X):
-            _qc.sparse_qc_csc(
-                X.indptr.data.ptr,
-                X.indices.data.ptr,
-                X.data.data.ptr,
-                sums_cells.data.ptr,
-                sums_genes.data.ptr,
-                genes_per_cell.data.ptr,
-                cells_per_gene.data.ptr,
-                int(X.shape[1]),
-                int(cp.dtype(X.data.dtype).itemsize),
-            )
-
+            sparse_qc = _qc.sparse_qc_csc
         else:
             raise ValueError("Please use a csr or csc matrix")
+        sparse_qc(
+            X.indptr.data.ptr,
+            X.indices.data.ptr,
+            X.data.data.ptr,
+            sums_cells.data.ptr,
+            sums_genes.data.ptr,
+            genes_per_cell.data.ptr,
+            cells_per_gene.data.ptr,
+            int(X.shape[1]),
+            int(cp.dtype(X.data.dtype).itemsize),
+        )
     else:
         from rapids_singlecell._cuda import _qc_cuda as _qc
 

src/rapids_singlecell/preprocessing/_utils.py

@@ -185,8 +185,8 @@ def _mean_var_dense_dask(X, axis):
     from ._kernels._mean_var_kernel import mean_sum, sq_sum
 
     def __mean_var(X_part):
-        var = sq_sum(X=X_part, axis=axis)
-        mean = mean_sum(X=X_part, axis=axis)
+        var = sq_sum(X_part, axis=axis)
+        mean = mean_sum(X_part, axis=axis)
         if axis == 0:
             return cp.vstack([mean, var])[None, ...]
         else:
@@ -217,8 +217,8 @@ def __mean_var(X_part):
 def _mean_var_dense(X, axis):
     from ._kernels._mean_var_kernel import mean_sum, sq_sum
 
-    var = sq_sum(X=X, axis=axis)
-    mean = mean_sum(X=X, axis=axis)
+    var = sq_sum(X, axis=axis)
+    mean = mean_sum(X, axis=axis)
     mean = mean / X.shape[axis]
     var = var / X.shape[axis]
     var -= cp.power(mean, 2)