fix precommit (#881)

* update

* update

* update

* update

* remove 3.9 from test

* remove py39 from lint

* add release note

.github/workflows/lint.yml

@@ -15,10 +15,10 @@ jobs:
             - uses: actions/checkout@v3
               with:
                   submodules: true
-            - name: Set up Python 3.9
+            - name: Set up Python 3.11
               uses: actions/setup-python@v4
               with:
-                  python-version: 3.9
+                  python-version: 3.11
 
             - uses: actions/cache@v3
               with:

.github/workflows/test.yml

@@ -20,7 +20,7 @@ jobs:
         strategy:
             fail-fast: false
             matrix:
-                python: [3.9, "3.10", "3.11"]
+                python: ["3.10", "3.11", "3.12"]
                 os: [ubuntu-latest]
                 include:
                     - python: "3.12"

.pre-commit-config.yaml

@@ -7,7 +7,7 @@ default_stages:
 minimum_pre_commit_version: 2.9.3
 repos:
     - repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.6.2
+      rev: v0.6.3
       hooks:
           - id: ruff
             types_or: [python, pyi, jupyter]
@@ -19,7 +19,7 @@ repos:
       hooks:
           - id: blacken-docs
     - repo: https://github.com/pre-commit/mirrors-mypy
-      rev: v1.11.1
+      rev: v1.11.2
       hooks:
           - id: mypy
             additional_dependencies: [numpy, pandas, types-requests]

docs/release/notes-1.6.1.rst

@@ -6,4 +6,5 @@ Maintenance
 
 - The Squidpy `interactive` module has been removed. Please use the ``napari-spatialdata`` plugin instead. You can find it `here <https://spatialdata.scverse.org/projects/napari/en/latest/index.html>`__  `@giovp <https://github.com/giovp>`__
   `#877 <https://github.com/scverse/squidpy/pull/877>`__
-
+- Drop support for Python 3.9 `@giovp <https://github.com/giovp>`__
+  `#881 <https://github.com/scverse/squidpy/pull/881>`__

pyproject.toml

@@ -18,8 +18,9 @@ classifiers = [
     "Operating System :: MacOS :: MacOS X",
     "Typing :: Typed",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "Environment :: Console",
     "Framework :: Jupyter",
     "Intended Audience :: Science/Research",

src/squidpy/gr/_build.py

@@ -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)  # type: ignore[assignment]  # invert
+        ixs = np.argsort(ixs)  # invert
         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/_ppatterns.py

@@ -164,7 +164,7 @@ def extract_obsm(adata: AnnData, ixs: int | Sequence[int] | None) -> tuple[NDArr
         if layer not in adata.obsm:
             raise KeyError(f"Key `{layer!r}` not found in `adata.obsm`.")
         if ixs is None:
-            ixs = np.arange(adata.obsm[layer].shape[1])  # type:ignore[assignment]
+            ixs = np.arange(adata.obsm[layer].shape[1])
         ixs = list(np.ravel([ixs]))
         return adata.obsm[layer][:, ixs].T, ixs
 
@@ -429,10 +429,10 @@ def co_occurrence(
     n_splits = max(min(n_splits, n_obs), 1)
 
     # split array and labels
-    spatial_splits = tuple(s for s in np.array_split(spatial, n_splits, axis=0) if len(s))  # type:ignore[arg-type]
-    labs_splits = tuple(s for s in np.array_split(labs, n_splits, axis=0) if len(s))  # type:ignore[arg-type]
+    spatial_splits = tuple(s for s in np.array_split(spatial, n_splits, axis=0) if len(s))
+    labs_splits = tuple(s for s in np.array_split(labs, n_splits, axis=0) if len(s))
     # create idx array including unique combinations and self-comparison
-    x, y = np.triu_indices_from(np.empty((n_splits, n_splits)))  # type:ignore[arg-type]
+    x, y = np.triu_indices_from(np.empty((n_splits, n_splits)))
     idx_splits = list(zip(x, y))
 
     n_jobs = _get_n_cores(n_jobs)
@@ -578,7 +578,7 @@ def _g_moments(w: spmatrix | NDArrayA) -> tuple[float, float, float]:
 
     # s1
     t = w.transpose() + w
-    t2 = t.multiply(t)  # type:ignore[union-attr]
+    t2 = t.multiply(t)
     s1 = t2.sum() / 2.0
 
     # s2

src/squidpy/gr/_sepal.py

@@ -182,8 +182,8 @@ def _score_helper(
 
     score, sparse = [], issparse(vals)
     for i in ixs:
-        conc = vals[:, i].toarray().flatten() if sparse else vals[:, i].copy()  # type:ignore[union-attr]
-        conc = vals[:, i].toarray().flatten() if sparse else vals[:, i].copy()  # type:ignore[union-attr]
+        conc = vals[:, i].toarray().flatten() if sparse else vals[:, i].copy()
+        conc = vals[:, i].toarray().flatten() if sparse else vals[:, i].copy()
         time_iter = _diffusion(conc, fun, n_iter, sat, sat_idx, unsat, unsat_idx, dt=dt, thresh=thresh)
         score.append(dt * time_iter)
 

src/squidpy/im/_container.py

@@ -983,12 +983,12 @@ def show(
         arr = arr.sel(z=library_ids)
 
         if channel is not None:
-            channel = np.asarray([channel]).ravel()  # type: ignore[assignment]
+            channel = np.asarray([channel]).ravel()
             if not len(channel):  # type: ignore[arg-type]
                 raise ValueError("No channels have been selected.")
             arr = arr[{arr.dims[-1]: channel}]
         else:
-            channel = np.arange(arr.shape[-1])  # type: ignore[assignment]
+            channel = np.arange(arr.shape[-1])
         if TYPE_CHECKING:
             assert isinstance(channel, Sequence)
 

src/squidpy/im/_feature_mixin.py

@@ -352,14 +352,14 @@ def convert_to_full_image_coordinates(x: NDArrayA, y: NDArrayA) -> NDArrayA:
             y_slc, x_slc = coord.to_image_coordinates(padding).slice
 
             # relative coordinates
-            y = (y - np.min(y)) / (np.max(y) - np.min(y))
-            x = (x - np.min(x)) / (np.max(x) - np.min(x))
+            y = (y - np.min(y)) / (np.max(y) - np.min(y))  # type:ignore[operator]
+            x = (x - np.min(x)) / (np.max(x) - np.min(x))  # type:ignore[operator]
 
             # coordinates in the uncropped image
             y = coord.slice[0].start + (y_slc.stop - y_slc.start) * y
             x = coord.slice[1].start + (x_slc.stop - x_slc.start) * x
 
-            return np.c_[x, y]  # type: ignore[no-any-return]
+            return np.c_[x, y]
 
         label_layer = self._get_layer(label_layer)
         library_id = self._get_library_id(library_id)

src/squidpy/im/_segment.py

@@ -96,7 +96,7 @@ def _postcondition(img: NDArrayA | da.Array) -> NDArrayA | da.Array:
     def _(self, img: NDArrayA, **kwargs: Any) -> NDArrayA:
         chunks = kwargs.pop("chunks", None)
         if chunks is not None:
-            return self.segment(da.asarray(img).rechunk(chunks), **kwargs)  # type: ignore[no-any-return]
+            return self.segment(da.asarray(img).rechunk(chunks), **kwargs)
 
         img = SegmentationModel._precondition(img)
         img = self._segment(img, **kwargs)

src/squidpy/pl/_ligrec.py

@@ -42,7 +42,7 @@ 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 = (size_range - np.min(size_range)) / (np.max(size_range) - np.min(size_range))  # type:ignore[operator]
         # no point in showing dot of size 0
         size_range = size_range[1:]
 

src/squidpy/pl/_spatial_utils.py

@@ -711,8 +711,8 @@ def _map_color_seg(
     if isinstance(color_vector.dtype, CategoricalDtype):
         if isinstance(na_color, tuple) and len(na_color) == 4 and np.any(color_source_vector.isna()):
             cell_id[color_source_vector.isna()] = 0
-        val_im: NDArrayA = map_array(seg, cell_id, color_vector.codes + 1)  # type: ignore[union-attr]
-        cols = colors.to_rgba_array(color_vector.categories)  # type: ignore[union-attr]
+        val_im: NDArrayA = map_array(seg, cell_id, color_vector.codes + 1)
+        cols = colors.to_rgba_array(color_vector.categories)
     else:
         val_im = map_array(seg, cell_id, cell_id)  # replace with same seg id to remove missing segs
         try:
@@ -960,7 +960,7 @@ def _plot_scatter(
             coords[:, 0],
             coords[:, 1],
             s=outline_params.bg_size,
-            c=outline_params.bg_color,  # type: ignore[arg-type]
+            c=outline_params.bg_color,
             rasterized=sc_settings._vector_friendly,
             cmap=cmap_params.cmap,
             norm=norm,
@@ -971,7 +971,7 @@ def _plot_scatter(
             coords[:, 0],
             coords[:, 1],
             s=outline_params.gap_size,
-            c=outline_params.gap_color,  # type: ignore[arg-type]
+            c=outline_params.gap_color,
             rasterized=sc_settings._vector_friendly,
             cmap=cmap_params.cmap,
             norm=norm,

tox.ini

@@ -41,9 +41,9 @@ sort = Miss
 
 [gh-actions]
 python =
-    3.8: py38
-    3.9: py39
     3.10: py310
+    3.11: py311
+    3.12: py312
 
 [gh-actions:env]
 PLATFORM =
@@ -55,7 +55,7 @@ isolated_build = True
 envlist =
     covclean
     lint
-    py{3.9,3.10}-{linux,macos}
+    py{3.10,3.11}-{linux,macos}
     coverage
     readme
     check-docs
@@ -93,7 +93,7 @@ deps =
     coverage
     diff_cover
 skip_install = true
-depends = py{38,39,310}-{linux,macos}
+depends = py{310,311,312}-{linux,macos}
 parallel_show_output = True
 commands =
     coverage report --omit="tox/*"