Backport PR #3795 on branch 1.11.x (ci: update Ruff) (#3796)

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

Author: flying-sheep

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.7
+    rev: v0.13.0
     hooks:
     -   id: ruff-check
         args: ["--fix"]

benchmarks/benchmarks/_utils.py

@@ -28,7 +28,7 @@ class ParamSkipper(Protocol):
         def __call__(self, **skipped: AbstractSet) -> Callable[[C], C]: ...
 
     Dataset = Literal["pbmc68k_reduced", "pbmc3k", "bmmc", "lung93k"]
-    KeyX = Literal[None, "off-axis"]
+    KeyX = Literal["off-axis"] | None
     KeyCount = Literal["counts", "counts-off-axis"]
 
 

pyproject.toml

@@ -215,6 +215,7 @@ exclude_also = [
 src = [ "src" ]
 
 [tool.ruff.format]
+preview = true
 docstring-code-format = true
 
 [tool.ruff.lint]

src/scanpy/_compat.py

@@ -117,6 +117,7 @@ def old_positionals(*old_positionals: str):
 
     def add_note(exc: BaseException, note: str) -> None:
         exc.add_note(note)
+
 else:
 
     def add_note(exc: BaseException, note: str) -> None:

src/scanpy/_settings/__init__.py

@@ -83,7 +83,7 @@ class SettingsMeta(SingletonMeta, type):
     _cachedir: Path
     _datasetdir: Path
     _figdir: Path
-    _cache_compression: Literal["lzf", "gzip", None]
+    _cache_compression: Literal["lzf", "gzip"] | None
     _max_memory: float
     _n_jobs: int
     _categories_to_ignore: list[str]
@@ -238,12 +238,14 @@ def figdir(cls, figdir: Path | str) -> None:
         cls._figdir = Path(figdir)
 
     @property
-    def cache_compression(cls) -> Literal["lzf", "gzip", None]:
+    def cache_compression(cls) -> Literal["lzf", "gzip"] | None:
         """Compression for `sc.read(..., cache=True)` (default `'lzf'`)."""
         return cls._cache_compression
 
     @cache_compression.setter
-    def cache_compression(cls, cache_compression: Literal["lzf", "gzip", None]) -> None:
+    def cache_compression(
+        cls, cache_compression: Literal["lzf", "gzip"] | None
+    ) -> None:
         if cache_compression not in {"lzf", "gzip", None}:
             msg = (
                 f"`cache_compression` ({cache_compression}) "

src/scanpy/external/exporting.py

@@ -440,24 +440,24 @@ def _export_paga_to_spring(adata, paga_coords, outpath) -> None:
     for i, name, xy, color, size, cells in zip(
         range(len(names)), names, coords, colors, sizes, cell_groups, strict=True
     ):
-        nodes.append(
-            {
-                "index": i,
-                "size": int(size),
-                "color": color,
-                "coordinates": xy,
-                "cells": cells,
-                "name": name,
-            }
-        )
+        nodes.append({
+            "index": i,
+            "size": int(size),
+            "color": color,
+            "coordinates": xy,
+            "cells": cells,
+            "name": name,
+        })
 
     # make link list, avoid redundant encoding (graph is undirected)
     links = []
     for source, target, weight in zip(sources, targets, weights, strict=True):
         if source < target and weight > min_edge_weight_save:
-            links.append(
-                {"source": int(source), "target": int(target), "weight": float(weight)}
-            )
+            links.append({
+                "source": int(source),
+                "target": int(target),
+                "weight": float(weight),
+            })
 
     # save data about edge weights
     edge_weight_meta = {

src/scanpy/get/_aggregated.py

@@ -393,9 +393,9 @@ def _combine_categories(
 
     # It's like np.concatenate([x for x in product(*[range(n) for n in n_categories])])
     code_combinations = np.indices(n_categories).reshape(len(n_categories), -1)
-    result_categories = pd.Index(
-        ["_".join(map(str, x)) for x in product(*[df[c].cat.categories for c in cols])]
-    )
+    result_categories = pd.Index([
+        "_".join(map(str, x)) for x in product(*[df[c].cat.categories for c in cols])
+    ])
 
     # Dataframe with unique combination of categories for each row
     new_label_df = pd.DataFrame(

src/scanpy/plotting/_anndata.py

@@ -1344,14 +1344,10 @@ def heatmap(  # noqa: PLR0912, PLR0913, PLR0915
 
         if categorical:
             groupby_ax = fig.add_subplot(axs[1, 0])
-            (
-                label2code,
-                ticks,
-                labels,
-                groupby_cmap,
-                norm,
-            ) = _plot_categories_as_colorblocks(
-                groupby_ax, obs_tidy, colors=groupby_colors, orientation="left"
+            label2code, ticks, _labels, groupby_cmap, norm = (
+                _plot_categories_as_colorblocks(
+                    groupby_ax, obs_tidy, colors=groupby_colors, orientation="left"
+                )
             )
 
             # add lines to main heatmap
@@ -1437,14 +1433,10 @@ def heatmap(  # noqa: PLR0912, PLR0913, PLR0915
 
         if categorical:
             groupby_ax = fig.add_subplot(axs[2, 0])
-            (
-                label2code,
-                ticks,
-                labels,
-                groupby_cmap,
-                norm,
-            ) = _plot_categories_as_colorblocks(
-                groupby_ax, obs_tidy, colors=groupby_colors, orientation="bottom"
+            label2code, ticks, _labels, groupby_cmap, norm = (
+                _plot_categories_as_colorblocks(
+                    groupby_ax, obs_tidy, colors=groupby_colors, orientation="bottom"
+                )
             )
             # add lines to main heatmap
             line_positions = (
@@ -1701,7 +1693,7 @@ def tracksplot(  # noqa: PLR0912, PLR0913, PLR0915
         ax.spines["top"].set_visible(False)
         ax.spines["bottom"].set_visible(False)
         ax.grid(visible=False)
-        ymin, ymax = ax.get_ylim()
+        _ymin, ymax = ax.get_ylim()
         ymax = int(ymax)
         ax.set_yticks([ymax])
         ax.set_yticklabels([str(ymax)], ha="left", va="top")
@@ -1730,7 +1722,7 @@ def tracksplot(  # noqa: PLR0912, PLR0913, PLR0915
 
     groupby_ax = fig.add_subplot(axs2[1])
 
-    label2code, ticks, labels, groupby_cmap, norm = _plot_categories_as_colorblocks(
+    _label2code, ticks, _labels, groupby_cmap, norm = _plot_categories_as_colorblocks(
         groupby_ax, obs_tidy.T, colors=groupby_colors, orientation="bottom"
     )
     # add lines to plot
@@ -2326,9 +2318,10 @@ def _reorder_categories_after_dendrogram(
             position = var_groups.positions[idx]
             _var_names = var_names[position[0] : position[1] + 1]
             var_names_idx_ordered.extend(range(position[0], position[1] + 1))
-            positions_ordered.append(
-                (position_start, position_start + len(_var_names) - 1)
-            )
+            positions_ordered.append((
+                position_start,
+                position_start + len(_var_names) - 1,
+            ))
             position_start += len(_var_names)
             labels_ordered.append(var_groups.labels[idx])
         var_groups = VarGroups(labels_ordered, positions_ordered)
@@ -2662,7 +2655,7 @@ def _plot_colorbar(mappable, fig, subplot_spec, max_cbar_height: float = 4.0):
     color bar ax
 
     """
-    width, height = fig.get_size_inches()
+    _width, height = fig.get_size_inches()
     if height > max_cbar_height:
         # to make the colorbar shorter, the
         # ax is split and the lower portion is used.

src/scanpy/plotting/_qc.py

@@ -98,7 +98,7 @@ def highest_expr_genes(
         # figsize is hardcoded to produce a tall image. To change the fig size,
         # a matplotlib.axes.Axes object needs to be passed.
         height = (n_top * 0.2) + 1.5
-        fig, ax = plt.subplots(figsize=(5, height))
+        _fig, ax = plt.subplots(figsize=(5, height))
     sns.boxplot(data=counts_top_genes, orient="h", ax=ax, fliersize=1, **kwds)
     ax.set_xlabel("% of total counts")
     if log:

src/scanpy/plotting/_tools/paga.py

@@ -258,9 +258,10 @@ def _compute_pos(  # noqa: PLR0912
             # I don't know why this is necessary
             # np.random.seed(random_state)
             if init_pos is None:
-                init_coords = random_state.random_sample(
-                    (adjacency_solid.shape[0], 2)
-                ).tolist()
+                init_coords = random_state.random_sample((
+                    adjacency_solid.shape[0],
+                    2,
+                )).tolist()
             else:
                 init_pos = init_pos.copy()
                 # this is a super-weird hack that is necessary as igraph’s
@@ -582,7 +583,7 @@ def is_flat(x):
         )
 
     if plot:
-        axs, panel_pos, draw_region_width, figure_width = _utils.setup_axes(
+        axs, panel_pos, draw_region_width, _figure_width = _utils.setup_axes(
             ax, panels=colors, colorbars=colorbars
         )
 
@@ -797,7 +798,7 @@ def _paga_graph(  # noqa: PLR0912, PLR0913, PLR0915
         and colors in adata.obs
         and isinstance(adata.obs[colors].dtype, CategoricalDtype)
     ):
-        asso_names, asso_matrix = _sc_utils.compute_association_matrix_of_groups(
+        _asso_names, asso_matrix = _sc_utils.compute_association_matrix_of_groups(
             adata,
             prediction=groups_key,
             reference=colors,
@@ -1268,14 +1269,12 @@ def moving_average(a):
 
         # groups bar
         ax_bounds = ax.get_position().bounds
-        groups_axis = plt.axes(
-            (
-                ax_bounds[0],
-                ax_bounds[1] - ax_bounds[3] / len(keys),
-                ax_bounds[2],
-                ax_bounds[3] / len(keys),
-            )
-        )
+        groups_axis = plt.axes((
+            ax_bounds[0],
+            ax_bounds[1] - ax_bounds[3] / len(keys),
+            ax_bounds[2],
+            ax_bounds[3] / len(keys),
+        ))
         groups = np.array(groups)[None, :]
         groups_axis.imshow(
             groups,
@@ -1307,14 +1306,12 @@ def moving_average(a):
         for ianno, anno in enumerate(annotations):
             if ianno > 0:
                 y_shift = ax_bounds[3] / len(keys) / 2
-            anno_axis = plt.axes(
-                (
-                    ax_bounds[0],
-                    ax_bounds[1] - (ianno + 2) * y_shift,
-                    ax_bounds[2],
-                    y_shift,
-                )
-            )
+            anno_axis = plt.axes((
+                ax_bounds[0],
+                ax_bounds[1] - (ianno + 2) * y_shift,
+                ax_bounds[2],
+                y_shift,
+            ))
             arr = np.array(anno_dict[anno])[None, :]
             if anno not in color_maps_annotations:
                 color_map_anno = (