Merge pull request #903 from scverse/bugfix/902-numpy-2-compat

numpy 2.0 and python >= 3.10 compatibility changes

.gitignore

@@ -5,6 +5,7 @@ __pycache__/
 
 # C extensions
 *.so
+*.pyc
 .DS_Store
 */.DS_Store
 .idea

.mypy.ini

@@ -1,6 +1,6 @@
 [mypy]
 mypy_path = squidpy
-python_version = 3.9
+python_version = 3.10
 plugins = numpy.typing.mypy_plugin
 
 ignore_errors = False

.pre-commit-config.yaml

@@ -1,6 +1,4 @@
 fail_fast: false
-default_language_version:
-    python: python3
 default_stages:
     - pre-commit
     - pre-push
@@ -24,3 +22,4 @@ repos:
           - id: mypy
             additional_dependencies: [numpy, pandas, types-requests]
             exclude: .scripts/ci/download_data.py|squidpy/datasets/_(dataset|image).py # See https://github.com/
+

.scripts/ci/download_data.py

@@ -51,7 +51,7 @@ def main(args: argparse.Namespace) -> None:
         obj = _maybe_download_data(func_name, path)
 
         # we could do without the AnnData check as well (1 less req. in tox.ini), but it's better to be safe
-        assert isinstance(obj, (AnnData, sq.im.ImageContainer)), type(obj)
+        assert isinstance(obj, AnnData | sq.im.ImageContainer), type(obj)
         assert path.is_file(), path
 
 

pyproject.toml

@@ -7,7 +7,7 @@ name = "squidpy"
 dynamic = ["version"]
 description = "Spatial Single Cell Analysis in Python"
 readme = "README.rst"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 license = {file = "LICENSE"}
 classifiers = [
     "Development Status :: 5 - Production/Stable",
@@ -42,7 +42,8 @@ authors = [
 ]
 maintainers = [
     {name = "Giovanni Palla", email = "[email protected]"},
-    {name = "Michal Klein", email = "[email protected]"}
+    {name = "Michal Klein", email = "[email protected]"},
+    {name = "Tim Treis", email = "[email protected]"}
 ]
 
 dependencies = [
@@ -117,6 +118,9 @@ include-package-data = true
 [tool.hatch.version]
 source = "vcs"
 
+[tool.hatch.metadata]
+allow-direct-references = true
+
 [tool.ruff]
 line-length = 120
 exclude = [

src/squidpy/__init__.py

@@ -1,14 +1,15 @@
 from __future__ import annotations
 
 from importlib import metadata
+from importlib.metadata import PackageMetadata
 
 from squidpy import datasets, gr, im, pl, read, tl
 
 try:
-    md = metadata.metadata(__name__)
-    __version__ = md.get("version", "")
-    __author__ = md.get("Author", "")
-    __maintainer__ = md.get("Maintainer-email", "")
+    md: PackageMetadata = metadata.metadata(__name__)
+    __version__ = md["Version"] if "Version" in md else ""
+    __author__ = md["Author"] if "Author" in md else ""
+    __maintainer__ = md["Maintainer-email"] if "Maintainer-email" in md else ""
 except ImportError:
     md = None  # type: ignore[assignment]
 

src/squidpy/_constants/_utils.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
 from abc import ABC, ABCMeta
-from collections.abc import Mapping
+from collections.abc import Callable, Mapping
 from enum import Enum, EnumMeta
 from functools import wraps
-from typing import Any, Callable
+from typing import Any
 
 
 def _pretty_raise_enum(cls: type[ModeEnum], fun: Callable[..., Any]) -> Callable[..., Any]:

src/squidpy/_docs.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
+from collections.abc import Callable
 from textwrap import dedent
-from typing import Any, Callable
+from typing import Any
 
 from docrep import DocstringProcessor
 

src/squidpy/_utils.py

@@ -5,13 +5,13 @@
 import functools
 import inspect
 import warnings
-from collections.abc import Generator, Hashable, Iterable, Sequence
+from collections.abc import Callable, Generator, Hashable, Iterable, Sequence
 from contextlib import contextmanager
 from enum import Enum
 from multiprocessing import Manager, cpu_count
 from queue import Queue
 from threading import Thread
-from typing import TYPE_CHECKING, Any, Callable
+from typing import TYPE_CHECKING, Any
 
 import joblib as jl
 import numpy as np

src/squidpy/datasets/_utils.py

@@ -2,20 +2,20 @@
 
 import os
 from abc import ABC, abstractmethod
-from collections.abc import Sequence
+from collections.abc import Callable, Sequence
 from dataclasses import dataclass, field
 from inspect import Parameter, Signature, signature
 from pathlib import Path
-from typing import Any, Callable, Union
+from typing import Any, TypeAlias, Union
 
 import anndata
 from anndata import AnnData
 from scanpy import logging as logg
 from scanpy import read
 from scanpy._utils import check_presence_download
 
-PathLike = Union[os.PathLike[str], str]
-Function_t = Callable[..., Union[AnnData, Any]]
+PathLike: TypeAlias = os.PathLike[str] | str
+Function_t: TypeAlias = Callable[..., AnnData | Any]
 
 
 @dataclass(frozen=True)

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)  # invert
+        ixs = np.argsort(ixs).tolist()  # 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:
@@ -400,7 +400,7 @@ def _build_connectivity(
             Dst = csr_matrix((dists, indices, indptr), shape=(N, N))
             # fmt: on
     else:
-        r = 1 if radius is None else radius if isinstance(radius, (int, float)) else max(radius)
+        r = 1 if radius is None else radius if isinstance(radius, int | float) else max(radius)
         tree = NearestNeighbors(n_neighbors=n_neighs, radius=r, metric="euclidean")
         tree.fit(coords)
 
@@ -519,7 +519,7 @@ def mask_graph(
     dists_key = Key.obsp.spatial_dist(spatial_key)
 
     # check polygon type
-    if not isinstance(polygon_mask, (Polygon, MultiPolygon)):
+    if not isinstance(polygon_mask, Polygon | MultiPolygon):
         raise ValueError(f"`polygon_mask` should be of type `Polygon` or `MultiPolygon`, got {type(polygon_mask)}")
 
     # get elements

src/squidpy/gr/_ligrec.py

@@ -8,7 +8,7 @@
 from functools import partial
 from itertools import product
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Any, Literal, Union
+from typing import TYPE_CHECKING, Any, Literal, TypeAlias, Union
 
 import numpy as np
 import pandas as pd
@@ -33,10 +33,11 @@
 
 __all__ = ["ligrec", "PermutationTest"]
 
-StrSeq = Sequence[str]
-SeqTuple = Sequence[tuple[str, str]]
-Interaction_t = Union[pd.DataFrame, Mapping[str, StrSeq], StrSeq, tuple[StrSeq, StrSeq], SeqTuple]
-Cluster_t = Union[StrSeq, tuple[StrSeq, StrSeq], SeqTuple]
+StrSeq: TypeAlias = Sequence[str]
+SeqTuple: TypeAlias = Sequence[tuple[str, str]]
+Interaction_t: TypeAlias = pd.DataFrame | Mapping[str, StrSeq] | StrSeq | tuple[StrSeq, StrSeq] | SeqTuple
+
+Cluster_t: TypeAlias = StrSeq | tuple[StrSeq, StrSeq] | SeqTuple
 
 SOURCE = "source"
 TARGET = "target"
@@ -263,7 +264,7 @@ def prepare(
             if isinstance(interactions[0], str):
                 interactions = list(product(interactions, repeat=2))
             elif len(interactions) == 2:
-                interactions = tuple(zip(*interactions))
+                interactions = tuple(zip(*interactions, strict=False))
 
             if not all(len(i) == 2 for i in interactions):
                 raise ValueError("Not all interactions are of length `2`.")
@@ -392,8 +393,8 @@ def test(
         data["clusters"] = data["clusters"].cat.remove_unused_categories()
         cat = data["clusters"].cat
 
-        cluster_mapper = dict(zip(cat.categories, range(len(cat.categories))))
-        gene_mapper = dict(zip(data.columns[:-1], range(len(data.columns) - 1)))  # -1 for 'clusters'
+        cluster_mapper = dict(zip(cat.categories, range(len(cat.categories)), strict=False))
+        gene_mapper = dict(zip(data.columns[:-1], range(len(data.columns) - 1), strict=False))  # -1 for 'clusters'
 
         data.columns = [gene_mapper[c] if c != "clusters" else c for c in data.columns]
         clusters_ = np.array([[cluster_mapper[c1], cluster_mapper[c2]] for c1, c2 in clusters], dtype=np.uint32)

src/squidpy/gr/_nhood.py

@@ -2,9 +2,9 @@
 
 from __future__ import annotations
 
-from collections.abc import Iterable, Sequence
+from collections.abc import Callable, Iterable, Sequence
 from functools import partial
-from typing import Any, Callable
+from typing import Any
 
 import networkx as nx
 import numba.types as nt
@@ -259,7 +259,7 @@ def centrality_scores(
     _assert_categorical_obs(adata, cluster_key)
     _assert_connectivity_key(adata, connectivity_key)
 
-    if isinstance(score, (str, Centrality)):
+    if isinstance(score, str | Centrality):
         centrality = [score]
     elif score is None:
         centrality = [c.s for c in Centrality]
@@ -386,7 +386,7 @@ def _interaction_matrix(
         cur_row = cats[i]
         cur_indices = indices_list[i]
         cur_data = data_list[i]
-        for j, val in zip(cur_indices, cur_data):
+        for j, val in zip(cur_indices, cur_data):  # noqa: B905
             cur_col = cats[j]
             output[cur_row, cur_col] += val
     return output

src/squidpy/gr/_ppatterns.py

@@ -164,8 +164,12 @@ 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])
-        ixs = list(np.ravel([ixs]))
+            ixs = list(range(adata.obsm[layer].shape[1]))
+        elif isinstance(ixs, int):
+            ixs = [ixs]
+        else:
+            ixs = list(ixs)
+
         return adata.obsm[layer][:, ixs].T, ixs
 
     if attr == "X":
@@ -297,11 +301,9 @@ def _occur_count(
         y = clust_y[idx_y]
         # Treat computing co-occurrence using the same split and different splits differently
         # Pairwise distance matrix for between the same split is symmetric and therefore only needs to be counted once
-        for i, j in zip(x, y):
-            if same_split:
-                co_occur[i, j] += 1
-            else:
-                co_occur[i, j] += 1
+        for i, j in zip(x, y):  # noqa: B905 # cannot use strict=False because of numba
+            co_occur[i, j] += 1
+            if not same_split:
                 co_occur[j, i] += 1
 
         # Prevent divison by zero errors when we have low cell counts/small intervals
@@ -416,24 +418,22 @@ def co_occurrence(
     n_obs = spatial.shape[0]
     if n_splits is None:
         size_arr = (n_obs**2 * spatial.itemsize) / 1024 / 1024  # calc expected mem usage
+        n_splits = 1
         if size_arr > 2000:
-            n_splits = 1
-            while 2048 < (n_obs / n_splits):
+            while (n_obs / n_splits) > 2048:
                 n_splits += 1
             logg.warning(
                 f"`n_splits` was automatically set to `{n_splits}` to "
                 f"prevent `{n_obs}x{n_obs}` distance matrix from being created"
             )
-        else:
-            n_splits = 1
-    n_splits = max(min(n_splits, n_obs), 1)
+    n_splits = int(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))
     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)))
-    idx_splits = list(zip(x, y))
+    idx_splits = list(zip(x, y, strict=False))
 
     n_jobs = _get_n_cores(n_jobs)
     start = logg.info(
@@ -578,7 +578,7 @@ def _g_moments(w: spmatrix | NDArrayA) -> tuple[float, float, float]:
 
     # s1
     t = w.transpose() + w
-    t2 = t.multiply(t)
+    t2 = t.multiply(t) if isinstance(t, spmatrix) else t * t
     s1 = t2.sum() / 2.0
 
     # s2

src/squidpy/gr/_sepal.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-from collections.abc import Sequence
-from typing import Callable, Literal
+from collections.abc import Callable, Sequence
+from typing import Literal
 
 import numpy as np
 import pandas as pd
@@ -151,7 +151,7 @@ def sepal(
 
     if sepal_score[key_added].isna().any():
         logg.warning("Found `NaN` in sepal scores, consider increasing `n_iter` to a higher value")
-    sepal_score.sort_values(by=key_added, ascending=False, inplace=True)
+    sepal_score = sepal_score.sort_values(by=key_added, ascending=False)
 
     if copy:
         logg.info("Finish", time=start)
@@ -180,10 +180,12 @@ def _score_helper(
     else:
         raise NotImplementedError(f"Laplacian for `{max_neighs}` neighbors is not yet implemented.")
 
-    score, sparse = [], issparse(vals)
+    score = []
     for i in ixs:
-        conc = vals[:, i].toarray().flatten() if sparse else vals[:, i].copy()
-        conc = vals[:, i].toarray().flatten() if sparse else vals[:, i].copy()
+        if isinstance(vals, spmatrix):
+            conc = vals[:, i].toarray().flatten()
+        else:
+            conc = 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/gr/_utils.py

@@ -96,7 +96,7 @@ def _create_sparse_df(
     )
 
     if not issparse(data):
-        pred = (lambda col: ~np.isnan(col)) if fill_value is np.nan else (lambda col: ~np.isclose(col, fill_value))
+        pred = (lambda col: ~np.isnan(col)) if np.isnan(fill_value) else (lambda col: ~np.isclose(col, fill_value))
         dtype = SparseDtype(data.dtype, fill_value=fill_value)
         n_rows, n_cols = data.shape
         arrays = []
@@ -236,13 +236,13 @@ def _extract_expression(
             res = adata[:, genes].layers[layer]
             if isinstance(res, AnnData):
                 res = res.X
-            elif not isinstance(res, (np.ndarray, spmatrix)):
+            elif not isinstance(res, np.ndarray | spmatrix):
                 raise TypeError(f"Invalid expression type `{type(res).__name__}`.")
 
     # handle views
     if isinstance(res, ArrayView):
         return np.asarray(res), genes
-    if isinstance(res, (SparseCSRView, SparseCSCView)):
+    if isinstance(res, SparseCSRView | SparseCSCView):
         mro = type(res).mro()
         if csr_matrix in mro:
             return csr_matrix(res), genes