ci: pre-commit autoupdate (#3931)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ ci:
 
 repos:
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.14.9
+    rev: v0.14.10
     hooks:
     -   id: ruff-check
         args: ["--fix"]
@@ -20,7 +20,7 @@ repos:
         - --sort-by-bibkey
         - --drop=abstract
 -   repo: https://github.com/biomejs/pre-commit
-    rev: v2.3.8
+    rev: v2.3.10
     hooks:
     -   id: biome-format
 -   repo: https://github.com/ComPWA/taplo-pre-commit

src/scanpy/get/get.py

@@ -96,7 +96,8 @@ def rank_genes_groups_df(
     for pts, name in {"pts": "pct_nz_group", "pts_rest": "pct_nz_reference"}.items():
         if pts in adata.uns[key]:
             pts_df = (
-                adata.uns[key][pts][group]
+                adata
+                .uns[key][pts][group]
                 .rename_axis(index="names")
                 .reset_index()
                 .melt(id_vars="names", var_name="group", value_name=name)

src/scanpy/plotting/_dotplot.py

@@ -203,7 +203,8 @@ def __init__(  # noqa: PLR0913
             # 2. compute mean expression value value
             if mean_only_expressed:
                 dot_color_df = (
-                    self.obs_tidy.mask(~obs_bool)
+                    self.obs_tidy
+                    .mask(~obs_bool)
                     .groupby(level=0, observed=True)
                     .mean()
                     .fillna(0)

src/scanpy/plotting/_matrixplot.py

@@ -169,7 +169,8 @@ def __init__(  # noqa: PLR0913
         if values_df is None:
             # compute mean value
             values_df = (
-                self.obs_tidy.groupby(level=0, observed=True)
+                self.obs_tidy
+                .groupby(level=0, observed=True)
                 .mean()
                 .loc[
                     self.categories_order

src/scanpy/plotting/_stacked_violin.py

@@ -392,7 +392,8 @@ def _mainplot(self, ax: Axes):
         # get mean values for color and transform to color values
         # using colormap
         _color_df = (
-            _matrix.groupby(level=0, observed=True)
+            _matrix
+            .groupby(level=0, observed=True)
             .median()
             .loc[
                 self.categories_order
@@ -495,7 +496,8 @@ def _make_rows_of_violinplots(
         # This format is convenient to aggregate per gene or per category
         # while making the violin plots.
         df = (
-            pd.DataFrame(_matrix.stack(future_stack=True))
+            pd
+            .DataFrame(_matrix.stack(future_stack=True))
             .reset_index()
             .rename(
                 columns={

src/scanpy/plotting/_tools/paga.py

@@ -166,7 +166,8 @@ def paga_compare(  # noqa: PLR0912, PLR0913
             )[0]
             coords = _basis[:, dims]
             pos = (
-                pd.DataFrame(coords, columns=["x", "y"], index=adata.obs_names)
+                pd
+                .DataFrame(coords, columns=["x", "y"], index=adata.obs_names)
                 .groupby(adata.obs[color], observed=True)
                 .median()
                 .sort_index()

src/scanpy/plotting/_tools/scatterplots.py

@@ -1134,7 +1134,8 @@ def _add_categorical_legend(  # noqa: PLR0913
         # identify centroids to put labels
 
         all_pos = (
-            pd.DataFrame(scatter_array, columns=["x", "y"])
+            pd
+            .DataFrame(scatter_array, columns=["x", "y"])
             .groupby(color_source_vector, observed=True)
             .median()
             # Have to sort_index since if observed=True and categorical is unordered

src/scanpy/preprocessing/_highly_variable_genes.py

@@ -71,7 +71,8 @@ def sum_and_sum_squares_clipped_from_block(block):
         return np.vstack(clip_square_sum(block, clip_val))[None, ...]
 
     squared_batch_counts_sum, batch_counts_sum = (
-        data_batch.map_blocks(
+        data_batch
+        .map_blocks(
             sum_and_sum_squares_clipped_from_block,
             new_axis=(1,),
             chunks=((1,) * n_blocks, (2,), (data_batch.shape[1],)),

src/scanpy/preprocessing/_pca/_dask.py

@@ -35,7 +35,8 @@ def fit(self, x: DaskArray) -> PCAEighDaskFit:
         >>> import dask.array as da
         >>> import scipy.sparse as sp
         >>> x = (
-        ...     da.array(sp.random(100, 200, density=0.3, dtype="int64").toarray())
+        ...     da
+        ...     .array(sp.random(100, 200, density=0.3, dtype="int64").toarray())
         ...     .rechunk((10, -1))
         ...     .map_blocks(sp.csr_matrix)
         ... )

src/scanpy/preprocessing/_scrublet/__init__.py

@@ -471,7 +471,8 @@ def _scrublet_call_doublets(  # noqa: PLR0913
         "parameters": {
             "expected_doublet_rate": expected_doublet_rate,
             "sim_doublet_ratio": (
-                adata_sim.uns.get("scrublet", {})
+                adata_sim.uns
+                .get("scrublet", {})
                 .get("parameters", {})
                 .get("sim_doublet_ratio", None)
             ),