[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

src/spatialdata_plot/pl/basic.py

@@ -1,61 +1,49 @@
 from __future__ import annotations
+
 from collections import OrderedDict
-from typing import Callable, Optional, Union, Any, Sequence
+from typing import Any, Optional, Union
+from collections.abc import Sequence
 
 import geopandas as gpd
-import matplotlib
-import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
 import scanpy as sc
 import spatialdata as sd
-import xarray as xr
 from anndata import AnnData
 from dask.dataframe.core import DataFrame as DaskDataFrame
 from geopandas import GeoDataFrame
+from matplotlib.axes import Axes
+from matplotlib.colors import Colormap, Normalize
+from matplotlib.figure import Figure
 from multiscale_spatial_image.multiscale_spatial_image import MultiscaleSpatialImage
 from pandas.api.types import is_categorical_dtype
 from spatial_image import SpatialImage
 from spatialdata import transform
-from spatialdata.models import Image2DModel, TableModel
+from spatialdata.models import Image2DModel
 from spatialdata.transformations import get_transformation
-from matplotlib.colors import Colormap
-from matplotlib.figure import Figure
-from matplotlib.gridspec import GridSpec
-from matplotlib import colors, patheffects, rcParams
-from matplotlib import pyplot as plt
-from matplotlib.axes import Axes
+
 from spatialdata_plot._accessor import register_spatial_data_accessor
-from spatialdata_plot.pp.utils import (
-    _get_instance_key,
-    _get_region_key,
-    _verify_plotting_tree,
-)
 from spatialdata_plot.pl.render import (
+    ImageRenderParams,
+    LabelsRenderParams,
+    ShapesRenderParams,
     _render_images,
     _render_labels,
     _render_points,
     _render_shapes,
-    LabelsRenderParams,
-    ImageRenderParams,
-    ShapesRenderParams,
 )
 from spatialdata_plot.pl.utils import (
-    _get_hex_colors_for_continous_values,
-    _get_random_hex_colors,
-    _get_subplots,
-    _maybe_set_colors,
-    Palette_t,
-    CmapParams,
     LegendParams,
+    Palette_t,
+    _FontSize,
+    _FontWeight,
+    _maybe_set_colors,
     _prepare_cmap_norm,
     _prepare_params_plot,
     _set_outline,
-    _FontSize,
-    _FontWeight,
 )
-from matplotlib.colors import ListedColormap, Normalize, to_rgb
-from dataclasses import dataclass
+from spatialdata_plot.pp.utils import (
+    _verify_plotting_tree,
+)
 
 
 @register_spatial_data_accessor("pl")
@@ -575,7 +563,7 @@ def show(
 
             # go through tree
             for cmd, params in render_cmds.items():
-                keys = list(sdata.images.keys())
+                list(sdata.images.keys())
 
                 if cmd == "render_images":
                     _render_images(

src/spatialdata_plot/pl/render.py

@@ -2,39 +2,38 @@
 
 from collections.abc import Iterable, Sequence
 from copy import copy
+from dataclasses import dataclass
 from functools import partial
-from typing import Callable, Optional, Union, Any
-from matplotlib.colors import Colormap
+from typing import Any, Optional, Union
+
 import matplotlib
-from spatialdata.models import TableModel
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
 import numpy as np
 import spatialdata as sd
-from scanpy._settings import settings as sc_settings
-import xarray as xr
+from geopandas import GeoDataFrame
 from matplotlib import colors
-from matplotlib.cm import get_cmap
-from matplotlib.colors import ListedColormap, Normalize, to_rgb, TwoSlopeNorm, ColorConverter
-from matplotlib.collections import Collection, PatchCollection
+from matplotlib.collections import PatchCollection
+from matplotlib.colors import (
+    ColorConverter,
+    ListedColormap,
+    Normalize,
+)
 from matplotlib.patches import Circle, Polygon
 from pandas.api.types import is_categorical_dtype
-from geopandas import GeoDataFrame
-from sklearn.decomposition import PCA
-from dataclasses import dataclass
+from scanpy._settings import settings as sc_settings
+from spatialdata.models import TableModel
+
 from spatialdata_plot.pl.utils import (
     CmapParams,
-    LegendParams,
     FigParams,
-    ScalebarParams,
+    LegendParams,
     OutlineParams,
+    ScalebarParams,
+    _decorate_axs,
+    _get_palette,
     _map_color_seg,
     _normalize,
     _set_color_source_vec,
-    _get_palette,
-    _decorate_axs,
 )
-from spatialdata_plot.pp.utils import _get_linear_colormap, _get_region_key
 
 Palette_t = Optional[Union[str, ListedColormap]]
 _Normalize = Union[Normalize, Sequence[Normalize]]
@@ -300,7 +299,6 @@ def _render_labels(
     # ax: matplotlib.axes.SubplotBase,
     # extent: dict[str, list[int]],
 ) -> None:
-
     # get instance and region keys
     instance_key = str(sdata.table.uns[TableModel.ATTRS_KEY][TableModel.INSTANCE_KEY])
     region_key = str(sdata.table.uns[TableModel.ATTRS_KEY][TableModel.REGION_KEY_KEY])

src/spatialdata_plot/pl/utils.py

@@ -1,39 +1,38 @@
 from __future__ import annotations
 
 from collections.abc import Iterable, Mapping, Sequence
+from copy import copy
+from dataclasses import dataclass
 from functools import partial
-from typing import Any, NamedTuple, Optional, Union, Literal, Type
 from types import MappingProxyType
-from copy import copy
+from typing import Any, Literal, Optional, Type, Union
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import xarray as xr
 from anndata import AnnData
 from cycler import Cycler, cycler
-from matplotlib import colors
-from matplotlib.colors import Colormap, ListedColormap, Normalize, to_rgba, TwoSlopeNorm
+from matplotlib import colors, patheffects
+from matplotlib import pyplot as plt
+from matplotlib import rcParams
+from matplotlib.axes import Axes
 from matplotlib.cm import get_cmap
+from matplotlib.collections import PatchCollection
+from matplotlib.colors import Colormap, ListedColormap, Normalize, TwoSlopeNorm, to_rgba
+from matplotlib.figure import Figure
+from matplotlib.gridspec import GridSpec
+from matplotlib_scalebar.scalebar import ScaleBar
 from numpy.random import default_rng
 from pandas.api.types import CategoricalDtype, is_categorical_dtype
+from scanpy.plotting._tools.scatterplots import _add_categorical_legend
+from scanpy.plotting.palettes import default_20, default_28, default_102
 from skimage.color import label2rgb
 from skimage.morphology import erosion, square
 from skimage.segmentation import find_boundaries
 from skimage.util import map_array
-from spatialdata._types import ArrayLike
 from spatialdata._logging import logger as logging
-from matplotlib import rcParams
-from scanpy.plotting.palettes import default_20, default_28, default_102
-from dataclasses import dataclass
-from matplotlib_scalebar.scalebar import ScaleBar
-from matplotlib.figure import Figure
-from matplotlib.gridspec import GridSpec
-from matplotlib import colors, patheffects, rcParams
-from matplotlib import pyplot as plt
-from matplotlib.axes import Axes
-from matplotlib.collections import Collection, PatchCollection
-from scanpy._settings import settings as sc_settings
-from scanpy.plotting._tools.scatterplots import _add_categorical_legend
+from spatialdata._types import ArrayLike
 
 Palette_t = Optional[Union[str, ListedColormap]]
 _Normalize = Union[Normalize, Sequence[Normalize]]
@@ -89,7 +88,6 @@ def _prepare_params_plot(
     scalebar_dx: float | Sequence[float] | None = None,
     scalebar_units: str | Sequence[str] | None = None,
 ) -> tuple[FigParams, ScalebarParams]:
-
     # len(list(itertools.product(*iter_panels)))
 
     # handle axes and size
@@ -193,7 +191,6 @@ def _prepare_cmap_norm(
     vmax: float | None = None,
     vcenter: float | None = None,
 ) -> dataclass:
-
     cmap = copy(get_cmap(cmap))
     cmap.set_bad("lightgray" if na_color is None else na_color)
 
@@ -460,7 +457,6 @@ def _map_color_seg(
     seg_boundaries: bool = False,
     na_color: str | tuple[float, ...] = (0, 0, 0, 0),
 ) -> ArrayLike:
-
     cell_id = np.array(cell_id)
 
     if is_categorical_dtype(color_vector):
@@ -601,7 +597,6 @@ def _decorate_axs(
     scalebar_units: Sequence[str] | None = None,
     scalebar_kwargs: Mapping[str, Any] = MappingProxyType({}),
 ) -> Axes:
-
     ax.set_yticks([])
     ax.set_xticks([])
     # ax.set_xlabel(fig_params.ax_labels[0])
@@ -654,7 +649,7 @@ def _decorate_axs(
 
 def _get_list(
     var: Any,
-    _type: Type[Any] | tuple[Type[Any], ...],
+    _type: type[Any] | tuple[type[Any], ...],
     ref_len: int | None = None,
     name: str | None = None,
 ) -> list[Any]: