Rely on Ruff for TYPE_CHECKING block mgmt

src/scanpy/cli.py

@@ -1,9 +1,9 @@
 from __future__ import annotations
 
-import collections.abc as cabc
 import os
 import sys
 from argparse import ArgumentParser, Namespace, _SubParsersAction
+from collections.abc import MutableMapping
 from functools import lru_cache, partial
 from pathlib import Path
 from shutil import which
@@ -27,7 +27,7 @@ def __init__(self, *args, _command: str, _runargs: dict[str, Any], **kwargs):
         )
 
 
-class _CommandDelegator(cabc.MutableMapping):
+class _CommandDelegator(MutableMapping):
     """\
     Provide the ability to delegate,
     but don’t calculate the whole list until necessary

src/scanpy/external/tl/_wishbone.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import collections.abc as cabc
+from collections.abc import Collection
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -11,7 +11,7 @@
 from ..._utils._doctests import doctest_needs
 
 if TYPE_CHECKING:
-    from collections.abc import Collection, Iterable
+    from collections.abc import Iterable
 
     from anndata import AnnData
 
@@ -115,7 +115,7 @@ def wishbone(
             f"Start cell {start_cell} not found in data. "
             "Please rerun with correct start cell."
         )
-    if isinstance(num_waypoints, cabc.Collection):
+    if isinstance(num_waypoints, Collection):
         diff = np.setdiff1d(num_waypoints, adata.obs.index)
         if diff.size > 0:
             logging.warning(

src/scanpy/plotting/_anndata.py

@@ -2,8 +2,8 @@
 
 from __future__ import annotations
 
-import collections.abc as cabc
 from collections import OrderedDict
+from collections.abc import Collection, Mapping, Sequence
 from itertools import product
 from typing import TYPE_CHECKING, get_args
 
@@ -38,7 +38,7 @@
 )
 
 if TYPE_CHECKING:
-    from collections.abc import Collection, Iterable, Mapping, Sequence
+    from collections.abc import Iterable
     from typing import Literal, Union
 
     from anndata import AnnData
@@ -220,7 +220,7 @@ def _scatter_obs(
         isinstance(layers, str) and layers in adata.layers.keys()
     ):
         layers = (layers, layers, layers)
-    elif isinstance(layers, cabc.Collection) and len(layers) == 3:
+    elif isinstance(layers, Collection) and len(layers) == 3:
         layers = tuple(layers)
         for layer in layers:
             if layer not in adata.layers.keys() and layer not in ["X", None]:
@@ -299,7 +299,7 @@ def _scatter_obs(
     palette_was_none = False
     if palette is None:
         palette_was_none = True
-    if isinstance(palette, cabc.Sequence) and not isinstance(palette, str):
+    if isinstance(palette, Sequence) and not isinstance(palette, str):
         if not is_color_like(palette[0]):
             palettes = palette
         else:
@@ -2665,7 +2665,7 @@ def _check_var_names_type(var_names, var_group_labels, var_group_positions):
     var_names, var_group_labels, var_group_positions
 
     """
-    if isinstance(var_names, cabc.Mapping):
+    if isinstance(var_names, Mapping):
         if var_group_labels is not None or var_group_positions is not None:
             logg.warning(
                 "`var_names` is a dictionary. This will reset the current "

src/scanpy/plotting/_baseplot_class.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-import collections.abc as cabc
+from collections.abc import Mapping
 from typing import TYPE_CHECKING, NamedTuple
 from warnings import warn
 
@@ -17,7 +17,7 @@
 from ._utils import check_colornorm, make_grid_spec
 
 if TYPE_CHECKING:
-    from collections.abc import Iterable, Mapping, Sequence
+    from collections.abc import Iterable, Sequence
     from typing import Literal, Self, Union
 
     import pandas as pd
@@ -1097,7 +1097,7 @@ def _update_var_groups(self) -> None:
 
         updates var_names, var_group_labels, var_group_positions
         """
-        if isinstance(self.var_names, cabc.Mapping):
+        if isinstance(self.var_names, Mapping):
             if self.has_var_groups:
                 logg.warning(
                     "`var_names` is a dictionary. This will reset the current "

src/scanpy/plotting/_tools/__init__.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
-import collections.abc as cabc
-from collections.abc import Mapping
+from collections.abc import Mapping, Sequence
 from copy import copy
 from typing import TYPE_CHECKING
 
@@ -38,7 +37,7 @@
 from .scatterplots import _panel_grid, embedding, pca
 
 if TYPE_CHECKING:
-    from collections.abc import Iterable, Sequence
+    from collections.abc import Iterable
     from typing import Literal
 
     from anndata import AnnData
@@ -1611,11 +1610,7 @@ def embedding_density(
 
     # if group is set, then plot it using multiple panels
     # (even if only one group is set)
-    if (
-        group is not None
-        and not isinstance(group, str)
-        and isinstance(group, cabc.Sequence)
-    ):
+    if group is not None and not isinstance(group, str) and isinstance(group, Sequence):
         if ax is not None:
             raise ValueError("Can only specify `ax` if no `group` sequence is given.")
         fig, gs = _panel_grid(hspace, wspace, ncols, len(group))

src/scanpy/plotting/_tools/paga.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-import collections.abc as cabc
 import warnings
+from collections.abc import Collection, Mapping, Sequence
 from pathlib import Path
 from types import MappingProxyType
 from typing import TYPE_CHECKING
@@ -26,7 +26,6 @@
 from .._utils import matrix
 
 if TYPE_CHECKING:
-    from collections.abc import Mapping, Sequence
     from typing import Any, Literal, Union
 
     from anndata import AnnData
@@ -506,14 +505,12 @@ def paga(
     groups_key = adata.uns["paga"]["groups"]
 
     def is_flat(x):
-        has_one_per_category = isinstance(x, cabc.Collection) and len(x) == len(
+        has_one_per_category = isinstance(x, Collection) and len(x) == len(
             adata.obs[groups_key].cat.categories
         )
         return has_one_per_category or x is None or isinstance(x, str)
 
-    if isinstance(colors, cabc.Mapping) and isinstance(
-        colors[next(iter(colors))], cabc.Mapping
-    ):
+    if isinstance(colors, Mapping) and isinstance(colors[next(iter(colors))], Mapping):
         # handle paga pie, remap string keys to integers
         names_to_ixs = {
             n: i for i, n in enumerate(adata.obs[groups_key].cat.categories)
@@ -554,7 +551,7 @@ def is_flat(x):
                 f"it needs to be one of {labels} not {root!r}."
             )
         root = list(labels).index(root)
-    if isinstance(root, cabc.Sequence) and root[0] in labels:
+    if isinstance(root, Sequence) and root[0] in labels:
         root = [list(labels).index(r) for r in root]
 
     # define the adjacency matrices
@@ -600,7 +597,7 @@ def is_flat(x):
             sct = _paga_graph(
                 adata,
                 axs[icolor],
-                colors=colors if isinstance(colors, cabc.Mapping) else c,
+                colors=colors if isinstance(colors, Mapping) else c,
                 solid_edges=solid_edges,
                 dashed_edges=dashed_edges,
                 transitions=transitions,
@@ -935,7 +932,7 @@ def _paga_graph(
             patheffects.withStroke(linewidth=fontoutline, foreground="w")
         ]
     # usual scatter plot
-    if not isinstance(colors[0], cabc.Mapping):
+    if not isinstance(colors[0], Mapping):
         n_groups = len(pos_array)
         sct = ax.scatter(
             pos_array[:, 0],
@@ -959,7 +956,7 @@ def _paga_graph(
     # else pie chart plot
     else:
         for ix, (xx, yy) in enumerate(zip(pos_array[:, 0], pos_array[:, 1])):
-            if not isinstance(colors[ix], cabc.Mapping):
+            if not isinstance(colors[ix], Mapping):
                 raise ValueError(
                     f"{colors[ix]} is neither a dict of valid "
                     "matplotlib colors nor a valid matplotlib color."

src/scanpy/plotting/_tools/scatterplots.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import collections.abc as cabc
 import inspect
 import sys
 from collections.abc import Mapping, Sequence  # noqa: TCH003
@@ -202,13 +201,13 @@ def embedding(
         title = [title] if isinstance(title, str) else list(title)
 
     # turn vmax and vmin into a sequence
-    if isinstance(vmax, str) or not isinstance(vmax, cabc.Sequence):
+    if isinstance(vmax, str) or not isinstance(vmax, Sequence):
         vmax = [vmax]
-    if isinstance(vmin, str) or not isinstance(vmin, cabc.Sequence):
+    if isinstance(vmin, str) or not isinstance(vmin, Sequence):
         vmin = [vmin]
-    if isinstance(vcenter, str) or not isinstance(vcenter, cabc.Sequence):
+    if isinstance(vcenter, str) or not isinstance(vcenter, Sequence):
         vcenter = [vcenter]
-    if isinstance(norm, Normalize) or not isinstance(norm, cabc.Sequence):
+    if isinstance(norm, Normalize) or not isinstance(norm, Sequence):
         norm = [norm]
 
     # Size
@@ -219,7 +218,7 @@ def embedding(
         # set as ndarray
         if (
             size is not None
-            and isinstance(size, (cabc.Sequence, pd.Series, np.ndarray))
+            and isinstance(size, (Sequence, pd.Series, np.ndarray))
             and len(size) == adata.shape[0]
         ):
             size = np.array(size, dtype=float)
@@ -245,9 +244,7 @@ def embedding(
     # Eg. ['Gene1', 'louvain', 'Gene2'].
     # component_list is a list of components [[0,1], [1,2]]
     if (
-        not isinstance(color, str)
-        and isinstance(color, cabc.Sequence)
-        and len(color) > 1
+        not isinstance(color, str) and isinstance(color, Sequence) and len(color) > 1
     ) or len(dimensions) > 1:
         if ax is not None:
             raise ValueError(

src/scanpy/plotting/_utils.py

@@ -1,8 +1,7 @@
 from __future__ import annotations
 
-import collections.abc as cabc
 import warnings
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Callable, Literal, TypedDict, Union, overload
 
 import matplotlib as mpl
@@ -23,7 +22,7 @@
 from . import palettes
 
 if TYPE_CHECKING:
-    from collections.abc import Collection, Mapping
+    from collections.abc import Collection
 
     from anndata import AnnData
     from matplotlib.colors import Colormap
@@ -445,12 +444,12 @@ def _set_colors_for_categorical_obs(
         # this creates a palette from a colormap. E.g. 'Accent, Dark2, tab20'
         cmap = plt.get_cmap(palette)
         colors_list = [to_hex(x) for x in cmap(np.linspace(0, 1, len(categories)))]
-    elif isinstance(palette, cabc.Mapping):
+    elif isinstance(palette, Mapping):
         colors_list = [to_hex(palette[k], keep_alpha=True) for k in categories]
     else:
         # check if palette is a list and convert it to a cycler, thus
         # it doesnt matter if the list is shorter than the categories length:
-        if isinstance(palette, cabc.Sequence):
+        if isinstance(palette, Sequence):
             if len(palette) < len(categories):
                 logg.warning(
                     "Length of palette colors is smaller than the number of "
@@ -551,7 +550,7 @@ def add_colors_for_categorical_sample_annotation(
 def plot_edges(axs, adata, basis, edges_width, edges_color, *, neighbors_key=None):
     import networkx as nx
 
-    if not isinstance(axs, cabc.Sequence):
+    if not isinstance(axs, Sequence):
         axs = [axs]
 
     if neighbors_key is None:
@@ -577,7 +576,7 @@ def plot_edges(axs, adata, basis, edges_width, edges_color, *, neighbors_key=Non
 
 
 def plot_arrows(axs, adata, basis, arrows_kwds=None):
-    if not isinstance(axs, cabc.Sequence):
+    if not isinstance(axs, Sequence):
         axs = [axs]
     v_prefix = next(
         (p for p in ["velocity", "Delta"] if f"{p}_{basis}" in adata.obsm), None
@@ -724,7 +723,7 @@ def setup_axes(
                 ax = plt.axes([left, bottom, width, height], projection="3d")
             axs.append(ax)
     else:
-        axs = ax if isinstance(ax, cabc.Sequence) else [ax]
+        axs = ax if isinstance(ax, Sequence) else [ax]
 
     return axs, panel_pos, draw_region_width, figure_width
 
@@ -763,7 +762,7 @@ def scatter_base(
     Depending on whether supplying a single array or a list of arrays,
     return a single axis or a list of axes.
     """
-    if isinstance(highlights, cabc.Mapping):
+    if isinstance(highlights, Mapping):
         highlights_indices = sorted(highlights)
         highlights_labels = [highlights[i] for i in highlights_indices]
     else:

src/scanpy/queries/_queries.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import collections.abc as cabc
+from collections.abc import Iterable
 from functools import singledispatch
 from types import MappingProxyType
 from typing import TYPE_CHECKING
@@ -12,7 +12,7 @@
 from ..get import rank_genes_groups_df
 
 if TYPE_CHECKING:
-    from collections.abc import Iterable, Mapping
+    from collections.abc import Mapping
     from typing import Any
 
     import pandas as pd
@@ -60,7 +60,7 @@ def simple_query(
     """
     if isinstance(attrs, str):
         attrs = [attrs]
-    elif isinstance(attrs, cabc.Iterable):
+    elif isinstance(attrs, Iterable):
         attrs = list(attrs)
     else:
         raise TypeError(f"attrs must be of type list or str, was {type(attrs)}.")

src/scanpy/tools/_marker_gene_overlap.py

@@ -4,7 +4,7 @@
 
 from __future__ import annotations
 
-import collections.abc as cabc
+from collections.abc import Set
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -190,7 +190,7 @@ def marker_gene_overlap(
     if normalize is not None and method != "overlap_count":
         raise ValueError("Can only normalize with method=`overlap_count`.")
 
-    if not all(isinstance(val, cabc.Set) for val in reference_markers.values()):
+    if not all(isinstance(val, Set) for val in reference_markers.values()):
         try:
             reference_markers = {
                 key: set(val) for key, val in reference_markers.items()