[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ default_stages:
 minimum_pre_commit_version: 2.9.3
 repos:
     - repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.8.1
+      rev: v0.9.1
       hooks:
           - id: ruff
             types_or: [python, pyi, jupyter]
@@ -17,7 +17,7 @@ repos:
       hooks:
           - id: blacken-docs
     - repo: https://github.com/pre-commit/mirrors-mypy
-      rev: v1.13.0
+      rev: v1.14.1
       hooks:
           - id: mypy
             additional_dependencies: [numpy, pandas, types-requests]

docs/_ext/typed_returns.py

@@ -12,7 +12,7 @@ def _process_return(lines: Iterable[str]) -> Iterator[str]:
         m = re.fullmatch(r"(?P<param>\w+)\s+:\s+(?P<type>[\w.]+)", line)
         if m:
             # Once this is in scanpydoc, we can use the fancy hover stuff
-            yield f'**{m["param"]}** : :class:`~{m["type"]}`'
+            yield f"**{m['param']}** : :class:`~{m['type']}`"
         else:
             yield line
 

pyproject.toml

@@ -51,7 +51,7 @@ dependencies = [
    "anndata>=0.9",
    "cycler>=0.11.0",
    "dask-image>=0.5.0",
-   "dask[array]>=2021.02.0",
+   "dask[array]>=2021.02.0,<=2024.11.2",
    "docrep>=0.3.1",
    "fsspec>=2021.11.0",
    "leidenalg>=0.8.2",

src/squidpy/datasets/_dataset.py

@@ -74,8 +74,7 @@
 )
 _slideseqv2 = AMetadata(
     name="slideseqv2",
-    doc_header="Pre-processed SlideseqV2 dataset from `Stickles et al "
-    "<https://doi.org/10.1038/s41587-020-0739-1>`__.",
+    doc_header="Pre-processed SlideseqV2 dataset from `Stickles et al <https://doi.org/10.1038/s41587-020-0739-1>`__.",
     shape=(41786, 4000),
     url="https://ndownloader.figshare.com/files/28242783",
 )

src/squidpy/gr/_build.py

@@ -6,7 +6,7 @@
 from collections.abc import Iterable  # noqa: F401
 from functools import partial
 from itertools import chain
-from typing import Any
+from typing import Any, cast
 
 import geopandas as gpd
 import numpy as np
@@ -145,16 +145,16 @@ def spatial_neighbors(
         - :attr:`anndata.AnnData.uns`  ``['{{key_added}}']`` - :class:`dict` containing parameters.
     """
     if isinstance(adata, SpatialData):
-        assert (
-            elements_to_coordinate_systems is not None
-        ), "Since `adata` is a :class:`spatialdata.SpatialData`, `elements_to_coordinate_systems` must not be `None`."
-        assert (
-            table_key is not None
-        ), "Since `adata` is a :class:`spatialdata.SpatialData`, `table_key` must not be `None`."
+        assert elements_to_coordinate_systems is not None, (
+            "Since `adata` is a :class:`spatialdata.SpatialData`, `elements_to_coordinate_systems` must not be `None`."
+        )
+        assert table_key is not None, (
+            "Since `adata` is a :class:`spatialdata.SpatialData`, `table_key` must not be `None`."
+        )
         elements, table = match_element_to_table(adata, list(elements_to_coordinate_systems), table_key)
-        assert table.obs_names.equals(
-            adata.tables[table_key].obs_names
-        ), "The spatialdata table must annotate all elements keys. Some elements are missing, please check the `elements_to_coordinate_systems` dictionary."
+        assert table.obs_names.equals(adata.tables[table_key].obs_names), (
+            "The spatialdata table must annotate all elements keys. Some elements are missing, please check the `elements_to_coordinate_systems` dictionary."
+        )
         regions, region_key, instance_key = get_table_keys(adata.tables[table_key])
         regions = [regions] if isinstance(regions, str) else regions
         ordered_regions_in_table = adata.tables[table_key].obs[region_key].unique()
@@ -237,7 +237,7 @@ def spatial_neighbors(
         for lib in libs:
             ixs.extend(np.where(adata.obs[library_key] == lib)[0])
             mats.append(_build_fun(adata[adata.obs[library_key] == lib]))
-        ixs = np.argsort(ixs).tolist()  # invert
+        ixs = cast(list[int], np.argsort(ixs).tolist())
         Adj = block_diag([m[0] for m in mats], format="csr")[ixs, :][:, ixs]
         Dst = block_diag([m[1] for m in mats], format="csr")[ixs, :][:, ixs]
     else:

src/squidpy/gr/_ligrec.py

@@ -441,8 +441,7 @@ def test(
 
         if corr_method is not None:
             logg.info(
-                f"Performing FDR correction across the `{corr_axis.v}` "
-                f"using method `{corr_method}` at level `{alpha}`"
+                f"Performing FDR correction across the `{corr_axis.v}` using method `{corr_method}` at level `{alpha}`"
             )
             res["pvalues"] = _fdr_correct(res["pvalues"], corr_method, corr_axis, alpha=alpha)
 

src/squidpy/im/_container.py

@@ -802,8 +802,7 @@ def generate_spot_crops(
                 obs_library_ids = list(adata.obs[library_id])
             except KeyError:
                 logg.debug(
-                    f"Unable to find library ids in `adata.obs[{library_id!r}]`. "
-                    f"Trying in `adata.uns[{spatial_key!r}]`"
+                    f"Unable to find library ids in `adata.obs[{library_id!r}]`. Trying in `adata.uns[{spatial_key!r}]`"
                 )
                 library_id = Key.uns.library_id(adata, spatial_key=spatial_key, library_id=library_id)
                 if not isinstance(library_id, str):
@@ -1622,7 +1621,7 @@ def _repr_html_(self) -> str:
             )
             s += "</p>"
             if i == 9 and i < len(self) - 1:  # show only first 10 layers
-                s += f"<p style={style!r}>and {len(self) - i  - 1} more...</p>"
+                s += f"<p style={style!r}>and {len(self) - i - 1} more...</p>"
                 break
 
         return s

src/squidpy/pl/_ligrec.py

@@ -41,8 +41,9 @@ def __init__(self, minn: float, delta: float, alpha: float | None, *args: Any, *
     def _plot_size_legend(self, size_legend_ax: Axes) -> None:
         y = self.BASE ** -((self.dot_max * self._delta) + self._minn)
         x = self.BASE ** -((self.dot_min * self._delta) + self._minn)
-        size_range = -(np.logspace(x, y, self.DEFAULT_NUM_LEGEND_DOTS + 1, base=10).astype(np.float64))
-        size_range = (size_range - np.min(size_range)) / (np.max(size_range) - np.min(size_range))
+        size_range = -np.logspace(x, y, self.DEFAULT_NUM_LEGEND_DOTS + 1, base=10, dtype=np.float64)
+        size_range = ((size_range - np.min(size_range)) / (np.max(size_range) - np.min(size_range))).astype(np.float64)
+
         # no point in showing dot of size 0
         size_range = size_range[1:]
 

src/squidpy/pl/_utils.py

@@ -178,9 +178,9 @@ def _warn_if_exists_obs(adata: AnnData, obs_key: str) -> None:
 @njit(cache=True, fastmath=True)
 def _point_inside_triangles(triangles: NDArrayA) -> np.bool_:
     # modified from napari
-    AB = triangles[:, 1, :] - triangles[:, 0, :]
-    AC = triangles[:, 2, :] - triangles[:, 0, :]
-    BC = triangles[:, 2, :] - triangles[:, 1, :]
+    AB: NDArrayA = triangles[:, 1, :] - triangles[:, 0, :]
+    AC: NDArrayA = triangles[:, 2, :] - triangles[:, 0, :]
+    BC: NDArrayA = triangles[:, 2, :] - triangles[:, 1, :]
 
     s_AB = -AB[:, 0] * triangles[:, 0, 1] + AB[:, 1] * triangles[:, 0, 0] >= 0
     s_AC = -AC[:, 0] * triangles[:, 0, 1] + AC[:, 1] * triangles[:, 0, 0] >= 0

src/squidpy/tl/_var_by_distance.py

@@ -3,7 +3,7 @@
 from collections.abc import Iterator
 from functools import reduce
 from itertools import product
-from typing import Any, Union
+from typing import Any, Union, cast
 
 import numpy as np
 import pandas as pd
@@ -67,7 +67,10 @@ def var_by_distance(
     elif isinstance(groups, list):
         anchor = groups
     elif isinstance(groups, np.ndarray):
-        anchor = groups.tolist()
+        # can't be a 2D array
+        if groups.ndim != 1:
+            raise ValueError(f"Expected a 1D array for 'groups', but got shape {groups.shape}.")
+        anchor = cast(list[str], groups.astype(str).tolist())
     else:
         raise TypeError(f"Invalid type for groups: {type(groups)}.")