Add PYI lints

benchmarks/benchmarks/_utils.py

@@ -14,15 +14,16 @@
 import scanpy as sc
 
 if TYPE_CHECKING:
-    from collections.abc import Callable, Sequence, Set
+    from collections.abc import Callable, Sequence
+    from collections.abc import Set as AbstractSet
     from typing import Literal, Protocol, TypeVar
 
     from anndata import AnnData
 
     C = TypeVar("C", bound=Callable)
 
     class ParamSkipper(Protocol):
-        def __call__(self, **skipped: Set) -> Callable[[C], C]: ...
+        def __call__(self, **skipped: AbstractSet) -> Callable[[C], C]: ...
 
     Dataset = Literal["pbmc68k_reduced", "pbmc3k", "bmmc", "lung93k"]
     KeyX = Literal[None, "off-axis"]
@@ -195,7 +196,7 @@ def param_skipper(
     b 5
     """
 
-    def skip(**skipped: Set) -> Callable[[C], C]:
+    def skip(**skipped: AbstractSet) -> Callable[[C], C]:
         skipped_combs = [
             tuple(record.values())
             for record in (

pyproject.toml

@@ -232,6 +232,7 @@ select = [
     "TID251",  # Banned imports
     "ICN",     # Follow import conventions
     "PTH",     # Pathlib instead of os.path
+    "PYI",     # Typing
     "PLR0917", # Ban APIs with too many positional parameters
     "FBT",     # No positional boolean parameters
     "PT",      # Pytest style
@@ -246,6 +247,8 @@ ignore = [
     "E262",
     # allow I, O, l as variable names -> I is the identity matrix, i, j, k, l is reasonable indexing notation
     "E741",
+    # `Literal["..."] | str` is useful for autocompletion
+    "PYI051",
 ]
 [tool.ruff.lint.per-file-ignores]
 # Do not assign a lambda expression, use a def

src/scanpy/_settings.py

@@ -19,7 +19,7 @@
 
     # Collected from the print_* functions in matplotlib.backends
     _Format = (
-        Literal["png", "jpg", "tif", "tiff"]
+        Literal["png", "jpg", "tif", "tiff"]  # noqa: PYI030
         | Literal["pdf", "ps", "eps", "svg", "svgz", "pgf"]
         | Literal["raw", "rgba"]
     )
@@ -340,7 +340,7 @@ def max_memory(self) -> int | float:
         return self._max_memory
 
     @max_memory.setter
-    def max_memory(self, max_memory: int | float):
+    def max_memory(self, max_memory: float):
         _type_check(max_memory, "max_memory", (int, float))
         self._max_memory = max_memory
 

src/scanpy/_utils/__init__.py

@@ -12,14 +12,13 @@
 import re
 import sys
 import warnings
-from collections import namedtuple
 from contextlib import contextmanager, suppress
 from enum import Enum
 from functools import partial, singledispatch, wraps
 from operator import mul, truediv
 from textwrap import dedent
 from types import MethodType, ModuleType
-from typing import TYPE_CHECKING, overload
+from typing import TYPE_CHECKING, Literal, NamedTuple, overload
 from weakref import WeakSet
 
 import h5py
@@ -296,6 +295,11 @@
 # --------------------------------------------------------------------------------
 
 
+class AssoResult(NamedTuple):
+    asso_names: list[str]
+    asso_matrix: NDArray[np.floating]
+
+
 def compute_association_matrix_of_groups(
     adata: AnnData,
     prediction: str,
@@ -304,7 +308,7 @@
     normalization: Literal["prediction", "reference"] = "prediction",
     threshold: float = 0.01,
     max_n_names: int | None = 2,
-):
+) -> AssoResult:
     """Compute overlaps between groups.
 
     See ``identify_groups`` for identifying the groups.
@@ -346,8 +350,8 @@
                 f"Ignoring category {cat!r} "
                 "as it’s in `settings.categories_to_ignore`."
             )
-    asso_names = []
-    asso_matrix = []
+    asso_names: list[str] = []
+    asso_matrix: list[list[float]] = []
     for ipred_group, pred_group in enumerate(adata.obs[prediction].cat.categories):
         if "?" in pred_group:
             pred_group = str(ipred_group)
@@ -380,13 +384,12 @@
             if asso_matrix[-1][i] > threshold
         ]
         asso_names += ["\n".join(name_list_pred[:max_n_names])]
-    Result = namedtuple(
-        "compute_association_matrix_of_groups", ["asso_names", "asso_matrix"]
-    )
-    return Result(asso_names=asso_names, asso_matrix=np.array(asso_matrix))
+    return AssoResult(asso_names=asso_names, asso_matrix=np.array(asso_matrix))
 
 
-def get_associated_colors_of_groups(reference_colors, asso_matrix):
+def get_associated_colors_of_groups(
+    reference_colors: Mapping[int, str], asso_matrix: NDArray[np.floating]
+) -> list[dict[str, float]]:
     return [
         {
             reference_colors[i_ref]: asso_matrix[i_pred, i_ref]

src/scanpy/cli.py

@@ -11,7 +11,7 @@
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from collections.abc import Generator, Mapping, Sequence
+    from collections.abc import Iterator, Mapping, Sequence
     from subprocess import CompletedProcess
     from typing import Any
 
@@ -64,7 +64,7 @@ def __delitem__(self, k: str) -> None:
 
     # These methods retrieve the command list or help with doing it
 
-    def __iter__(self) -> Generator[str, None, None]:
+    def __iter__(self) -> Iterator[str]:
         yield from self.parser_map
         yield from self.commands
 

src/scanpy/get/_aggregated.py

@@ -160,7 +160,7 @@ def median(self) -> Array:
         return np.array(medians)
 
 
-def _power(X: Array, power: float | int) -> Array:
+def _power(X: Array, power: float) -> Array:
     """\
     Generate elementwise power of a matrix.
 

src/scanpy/plotting/_anndata.py

@@ -101,15 +101,15 @@ def scatter(
     components: str | Collection[str] | None = None,
     projection: Literal["2d", "3d"] = "2d",
     legend_loc: _LegendLoc | None = "right margin",
-    legend_fontsize: int | float | _FontSize | None = None,
+    legend_fontsize: float | _FontSize | None = None,
     legend_fontweight: int | _FontWeight | None = None,
     legend_fontoutline: float | None = None,
     color_map: str | Colormap | None = None,
     palette: Cycler | ListedColormap | ColorLike | Sequence[ColorLike] | None = None,
     frameon: bool | None = None,
     right_margin: float | None = None,
     left_margin: float | None = None,
-    size: int | float | None = None,
+    size: float | None = None,
     marker: str | Sequence[str] = ".",
     title: str | Collection[str] | None = None,
     show: bool | None = None,
@@ -229,15 +229,15 @@ def _scatter_obs(
     components: str | Collection[str] | None = None,
     projection: Literal["2d", "3d"] = "2d",
     legend_loc: _LegendLoc | None = "right margin",
-    legend_fontsize: int | float | _FontSize | None = None,
+    legend_fontsize: float | _FontSize | None = None,
     legend_fontweight: int | _FontWeight | None = None,
     legend_fontoutline: float | None = None,
     color_map: str | Colormap | None = None,
     palette: Cycler | ListedColormap | ColorLike | Sequence[ColorLike] | None = None,
     frameon: bool | None = None,
     right_margin: float | None = None,
     left_margin: float | None = None,
-    size: int | float | None = None,
+    size: float | None = None,
     marker: str | Sequence[str] = ".",
     title: str | Collection[str] | None = None,
     show: bool | None = None,

src/scanpy/plotting/_stacked_violin.py

@@ -273,7 +273,7 @@ def style(
         cmap: Colormap | str | None | Empty = _empty,
         stripplot: bool | Empty = _empty,
         jitter: float | bool | Empty = _empty,
-        jitter_size: int | float | Empty = _empty,
+        jitter_size: float | Empty = _empty,
         linewidth: float | None | Empty = _empty,
         row_palette: str | None | Empty = _empty,
         density_norm: DensityNorm | Empty = _empty,
@@ -470,8 +470,8 @@ def _make_rows_of_violinplots(
         _matrix,
         colormap_array,
         _color_df,
-        x_spacer_size: float | int,
-        y_spacer_size: float | int,
+        x_spacer_size: float,
+        y_spacer_size: float,
         x_axis_order,
     ):
         import seaborn as sns  # Slow import, only import if called
@@ -699,7 +699,7 @@ def stacked_violin(
     cmap: Colormap | str | None = StackedViolin.DEFAULT_COLORMAP,
     stripplot: bool = StackedViolin.DEFAULT_STRIPPLOT,
     jitter: float | bool = StackedViolin.DEFAULT_JITTER,
-    size: int | float = StackedViolin.DEFAULT_JITTER_SIZE,
+    size: float = StackedViolin.DEFAULT_JITTER_SIZE,
     row_palette: str | None = StackedViolin.DEFAULT_ROW_PALETTE,
     density_norm: DensityNorm | Empty = _empty,
     yticklabels: bool = StackedViolin.DEFAULT_PLOT_YTICKLABELS,

src/scanpy/plotting/_tools/__init__.py

@@ -1209,7 +1209,7 @@ def rank_genes_groups_violin(
     split: bool = True,
     density_norm: DensityNorm = "width",
     strip: bool = True,
-    jitter: int | float | bool = True,
+    jitter: float | bool = True,
     size: int = 1,
     ax: Axes | None = None,
     show: bool | None = None,
@@ -1428,7 +1428,7 @@ def embedding_density(
     *,
     key: str | None = None,
     groupby: str | None = None,
-    group: str | Sequence[str] | None | None = "all",
+    group: str | Sequence[str] | None = "all",
     color_map: Colormap | str = "YlOrRd",
     bg_dotsize: int | None = 80,
     fg_dotsize: int | None = 180,

src/scanpy/plotting/_tools/paga.py

@@ -73,7 +73,7 @@ def paga_compare(
     components=None,
     projection: Literal["2d", "3d"] = "2d",
     legend_loc: _LegendLoc | None = "on data",
-    legend_fontsize: int | float | _FontSize | None = None,
+    legend_fontsize: float | _FontSize | None = None,
     legend_fontweight: int | _FontWeight = "bold",
     legend_fontoutline=None,
     color_map=None,
@@ -1053,7 +1053,7 @@ def paga_path(
     show_node_names: bool = True,
     show_yticks: bool = True,
     show_colorbar: bool = True,
-    legend_fontsize: int | float | _FontSize | None = None,
+    legend_fontsize: float | _FontSize | None = None,
     legend_fontweight: int | _FontWeight | None = None,
     normalize_to_zero_one: bool = False,
     as_heatmap: bool = True,

src/scanpy/plotting/_tools/scatterplots.py

@@ -94,7 +94,7 @@ def embedding(
     na_in_legend: bool = True,
     size: float | Sequence[float] | None = None,
     frameon: bool | None = None,
-    legend_fontsize: int | float | _FontSize | None = None,
+    legend_fontsize: float | _FontSize | None = None,
     legend_fontweight: int | _FontWeight = "bold",
     legend_loc: _LegendLoc | None = "right margin",
     legend_fontoutline: int | None = None,

src/scanpy/preprocessing/_scale.py

@@ -148,7 +148,6 @@ def scale_array(
     | tuple[
         np.ndarray | DaskArray, NDArray[np.float64] | DaskArray, NDArray[np.float64]
     ]
-    | DaskArray
 ):
     if copy:
         X = X.copy()

src/scanpy/preprocessing/_scrublet/sparse_utils.py

@@ -17,7 +17,7 @@
 
 def sparse_multiply(
     E: sparse.csr_matrix | sparse.csc_matrix | NDArray[np.float64],
-    a: float | int | NDArray[np.float64],
+    a: float | NDArray[np.float64],
 ) -> sparse.csr_matrix | sparse.csc_matrix:
     """multiply each row of E by a scalar"""
 

src/scanpy/tools/_marker_gene_overlap.py

@@ -4,7 +4,7 @@
 
 from __future__ import annotations
 
-from collections.abc import Set
+from collections.abc import Set as AbstractSet
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -187,7 +187,7 @@ 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, Set) for val in reference_markers.values()):
+    if not all(isinstance(val, AbstractSet) for val in reference_markers.values()):
         try:
             reference_markers = {
                 key: set(val) for key, val in reference_markers.items()

src/scanpy/tools/_rank_genes_groups.py

@@ -749,7 +749,7 @@ def filter_rank_genes_groups(
     use_raw: bool | None = None,
     key_added: str = "rank_genes_groups_filtered",
     min_in_group_fraction: float = 0.25,
-    min_fold_change: int | float = 1,
+    min_fold_change: float = 1,
     max_out_group_fraction: float = 0.5,
     compare_abs: bool = False,
 ) -> None:

src/scanpy/tools/_tsne.py

@@ -34,10 +34,10 @@ def tsne(
     n_pcs: int | None = None,
     *,
     use_rep: str | None = None,
-    perplexity: float | int = 30,
+    perplexity: float = 30,
     metric: str = "euclidean",
-    early_exaggeration: float | int = 12,
-    learning_rate: float | int = 1000,
+    early_exaggeration: float = 12,
+    learning_rate: float = 1000,
     random_state: AnyRandom = 0,
     use_fast_tsne: bool = False,
     n_jobs: int | None = None,