[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

src/scanpy/tools/_score_genes.py

@@ -4,18 +4,18 @@
 
 from typing import TYPE_CHECKING
 
+import numba
 import numpy as np
 import pandas as pd
-import numba
 
 from .. import logging as logg
-from .._compat import CSBase, old_positionals, njit
+from .._compat import CSBase, njit, old_positionals
 from .._utils import _check_use_raw, is_backed_type
 from ..get import _get_obs_rep
 
 if TYPE_CHECKING:
     from collections.abc import Callable, Generator, Sequence
-    from typing import Literal, Tuple, Any
+    from typing import Any, Literal
 
     from anndata import AnnData
     from numpy.typing import DTypeLike, NDArray
@@ -29,32 +29,38 @@
     _GetSubset = Callable[[_StrIdx], np.ndarray | CSBase]
 
 
-def _get_mean_columns(data: NDArray[Any], indicies: NDArray[np.int32], shape: Tuple) -> NDArray[np.float64]:
+def _get_mean_columns(
+    data: NDArray[Any], indicies: NDArray[np.int32], shape: tuple
+) -> NDArray[np.float64]:
     sums = np.zeros(shape[1], dtype=np.float64)
     counts = np.repeat(float(shape[0]), shape[1])
     for data_index in numba.prange(len(data)):
         if np.isnan(data[data_index]):
             counts[indicies[data_index]] -= 1.0
             continue
         sums[indicies[data_index]] += data[data_index]
-    #if we have row column nans return nan (not inf)
+    # if we have row column nans return nan (not inf)
     counts[counts == 0.0] = np.nan
-    return sums/counts
+    return sums / counts
+
 
-     
 @njit
-def _get_mean_rows(data: NDArray[Any], indptr: NDArray[np.int32], shape: Tuple) -> NDArray[np.float64]:
+def _get_mean_rows(
+    data: NDArray[Any], indptr: NDArray[np.int32], shape: tuple
+) -> NDArray[np.float64]:
     sums = np.zeros(shape[0], dtype=np.float64)
     counts = np.repeat(float(shape[1]), shape[0])
     for cur_row_index in numba.prange(shape[0]):
-        for data_index in numba.prange(indptr[cur_row_index], indptr[cur_row_index + 1]):
+        for data_index in numba.prange(
+            indptr[cur_row_index], indptr[cur_row_index + 1]
+        ):
             if np.isnan(data[data_index]):
                 counts[cur_row_index] -= 1.0
                 continue
             sums[cur_row_index] += data[data_index]
-    #if we have row from nans return nan (not inf)
+    # if we have row from nans return nan (not inf)
     counts[counts == 0.0] = np.nan
-    return sums/counts
+    return sums / counts
 
 
 @njit
@@ -64,7 +70,7 @@ def _sparse_nanmean(X: CSBase, axis: Literal[0, 1]) -> NDArray[np.float64]:
         msg = "X must be a compressed sparse matrix"
         raise TypeError(msg)
 
-    if axis==1:
+    if axis == 1:
         return _get_mean_rows(X.data, X.indptr, X.shape)
     else:
         return _get_mean_columns(X.data, X.indices, X.shape)