Maintainability quick wins: dedup, redundant copies, O(K^2) lookup

src/spatialdata_plot/pl/basic.py

@@ -46,6 +46,7 @@
     ChannelLegendEntry,
     CmapParams,
     ColorbarSpec,
+    ColorLike,
     GraphRenderParams,
     ImageRenderParams,
     LabelsRenderParams,
@@ -80,11 +81,6 @@
     save_fig,
 )
 
-# replace with
-# from spatialdata._types import ColorLike
-# once https://github.com/scverse/spatialdata/pull/689/ is in a release
-ColorLike = tuple[float, ...] | list[float] | str
-
 
 @register_spatial_data_accessor("pl")
 class PlotAccessor:

src/spatialdata_plot/pl/render.py

@@ -87,6 +87,16 @@
 
 _Normalize = Normalize | abc.Sequence[Normalize]
 
+# Shared body of the "blending multiple cmaps" warning. Emitted both when the user
+# supplies several cmaps and when a single cmap is broadcast across channels.
+_MULTI_CMAP_BLENDING_WARNING = (
+    "You're blending multiple cmaps. "
+    "If the plot doesn't look like you expect, it might be because your "
+    "cmaps go from a given color to 'white', and not to 'transparent'. "
+    "Therefore, the 'white' of higher layers will overlay the lower layers. "
+    "Consider using 'palette' instead."
+)
+
 
 def _get_top_data_array(element: xr.DataArray | DataTree) -> xr.DataArray:
     if isinstance(element, DataTree):
@@ -1136,9 +1146,9 @@ def _render_points(
         # if the points are colored by values in X (or a different layer), add the values to obs
         if col_for_color in matched_table.var_names:
             if table_layer is None:
-                adata_obs[col_for_color] = matched_table[:, col_for_color].X.flatten().copy()
+                adata_obs[col_for_color] = matched_table[:, col_for_color].X.flatten()
             else:
-                adata_obs[col_for_color] = matched_table[:, col_for_color].layers[table_layer].flatten().copy()
+                adata_obs[col_for_color] = matched_table[:, col_for_color].layers[table_layer].flatten()
         adata = AnnData(
             X=points[["x", "y"]].values,
             obs=adata_obs,
@@ -1742,13 +1752,7 @@ def _render_images(
         user_supplied_multi_cmaps = False
 
     if user_supplied_multi_cmaps:
-        logger.warning(
-            "You're blending multiple cmaps. "
-            "If the plot doesn't look like you expect, it might be because your "
-            "cmaps go from a given color to 'white', and not to 'transparent'. "
-            "Therefore, the 'white' of higher layers will overlay the lower layers. "
-            "Consider using 'palette' instead."
-        )
+        logger.warning(_MULTI_CMAP_BLENDING_WARNING)
 
     # Force nearest-neighbor at display time when the datashader reduction picked
     # a non-mean aggregation; otherwise imshow's default interpolation would smear it.
@@ -1864,7 +1868,9 @@ def _render_images(
             )
         layers = {}
         for ch_idx, ch in enumerate(channels):
-            layers[ch] = img.sel(c=ch).copy(deep=True).squeeze()
+            # No copy needed: this entry is only read (min/max) and then replaced
+            # by a fresh array (np.full or ch_norm(...)) below; img is never mutated.
+            layers[ch] = img.sel(c=ch).squeeze()
             if isinstance(render_params.cmap_params, list):
                 ch_norm = render_params.cmap_params[ch_idx].norm
             else:
@@ -1904,11 +1910,7 @@ def _render_images(
                 stacked = stacked[:, :, :3]
                 logger.warning(
                     "One cmap was given for multiple channels and is now used for each channel. "
-                    "You're blending multiple cmaps. "
-                    "If the plot doesn't look like you expect, it might be because your "
-                    "cmaps go from a given color to 'white', and not to 'transparent'. "
-                    "Therefore, the 'white' of higher layers will overlay the lower layers. "
-                    "Consider using 'palette' instead."
+                    + _MULTI_CMAP_BLENDING_WARNING
                 )
 
             _ax_show_and_transform(

src/spatialdata_plot/pl/render_params.py

@@ -15,10 +15,11 @@
 _DsReduction = Literal["sum", "mean", "any", "count", "std", "var", "max", "min"]
 _ImageDsReduction = Literal["max", "min", "mean", "mode", "first", "last", "var", "std"]
 
+# Canonical definition for the package; imported by basic.py and utils.py.
 # replace with
 # from spatialdata._types import ColorLike
 # once https://github.com/scverse/spatialdata/pull/689/ is in a release
-ColorLike = tuple[float, ...] | str
+ColorLike = tuple[float, ...] | list[float] | str
 
 
 # NOTE: defined here instead of utils to avoid circular import

src/spatialdata_plot/pl/utils.py

@@ -4,7 +4,7 @@
 import os
 import warnings
 from collections import Counter, OrderedDict
-from collections.abc import Callable, Iterable, Mapping, Sequence
+from collections.abc import Callable, Mapping, Sequence
 from copy import copy
 from functools import partial
 from pathlib import Path
@@ -78,6 +78,7 @@
     CmapParams,
     Color,
     ColorbarSpec,
+    ColorLike,
     FigParams,
     GraphRenderParams,
     ImageRenderParams,
@@ -93,11 +94,6 @@
 
 to_hex = partial(colors.to_hex, keep_alpha=True)
 
-# replace with
-# from spatialdata._types import ColorLike
-# once https://github.com/scverse/spatialdata/pull/689/ is in a release
-ColorLike = tuple[float, ...] | list[float] | str
-
 _GROUPS_IGNORED_WARNING = "Parameter 'groups' is ignored when 'color' is a literal color, not a column name."
 
 _RENDER_CMD_TO_CS_FLAG: dict[str, str] = {
@@ -996,44 +992,6 @@ def _set_outline(
     )
 
 
-def _get_subplots(num_images: int, ncols: int = 4, width: int = 4, height: int = 3) -> plt.Figure | plt.Axes:
-    """Set up the axs objects.
-
-    Parameters
-    ----------
-    num_images
-        Number of images to plot. Must be greater than 1.
-    ncols
-        Number of columns in the subplot grid, by default 4
-    width
-        Width of each subplot, by default 4
-
-    Returns
-    -------
-    Union[plt.Figure, plt.Axes]
-        Matplotlib figure and axes object.
-    """
-    if num_images < ncols:
-        nrows = 1
-        ncols = num_images
-    else:
-        nrows, reminder = divmod(num_images, ncols)
-
-        if nrows == 0:
-            nrows = 1
-        if reminder > 0:
-            nrows += 1
-
-    fig, axes = plt.subplots(nrows, ncols, figsize=(width * ncols, height * nrows))
-
-    if not isinstance(axes, Iterable):
-        axes = np.array([axes])
-
-    # get rid of the empty axes
-    _ = [ax.axis("off") for ax in axes.flatten()[num_images:]]
-    return fig, axes
-
-
 def _get_colors_for_categorical_obs(
     categories: Sequence[str | int],
     palette: ListedColormap | str | list[str] | None = None,
@@ -1503,7 +1461,7 @@ def _map_color_seg(
 
     if isinstance(color_vector.dtype, pd.CategoricalDtype):
         # Case A: users wants to plot a categorical column
-        val_im: ArrayLike = map_array(seg.copy(), cell_id, color_vector.codes + 1)
+        val_im: ArrayLike = map_array(seg, cell_id, color_vector.codes + 1)
         cols = colors.to_rgba_array(color_vector.categories)
     elif pd.api.types.is_numeric_dtype(color_vector.dtype):
         # Case B: user wants to plot a continous column
@@ -1515,7 +1473,7 @@ def _map_color_seg(
             normed_color_vector[~np.isnan(normed_color_vector)]
         )
         cols = cmap_params.cmap(normed_color_vector)
-        val_im = map_array(seg.copy(), cell_id, cell_id)
+        val_im = map_array(seg, cell_id, cell_id)
     else:
         # Case C: User didn't specify any colors
         if color_source_vector is not None and (
@@ -1524,12 +1482,12 @@ def _map_color_seg(
             and set(color_vector) == {na_color.get_hex_with_alpha()}
             and not na_color.color_modified_by_user()
         ):
-            val_im = map_array(seg.copy(), cell_id, cell_id)
+            val_im = map_array(seg, cell_id, cell_id)
             RNG = default_rng(42)
             cols = RNG.random((len(color_vector), 3))
         else:
             # Case D: User didn't specify a column to color by, but modified the na_color
-            val_im = map_array(seg.copy(), cell_id, cell_id)
+            val_im = map_array(seg, cell_id, cell_id)
             first_value = color_vector.iloc[0] if isinstance(color_vector, pd.Series) else color_vector[0]
             if _is_color_like(first_value):
                 # we have color-like values (e.g., hex or named colors)
@@ -1550,7 +1508,7 @@ def _map_color_seg(
         if outline_color_source_vector is not None:
             cat = pd.Categorical(outline_color_source_vector)
             cat_codes = cat.codes
-            outline_val_im: ArrayLike = map_array(seg.copy(), cell_id, cat_codes + 1)
+            outline_val_im: ArrayLike = map_array(seg, cell_id, cat_codes + 1)
             color_arr = np.asarray(outline_color_vector, dtype=object)
             # Pick the first per-cell hex for each category in one vectorized pass
             # (avoids `K × O(N)` Python loops on large label sets).
@@ -1572,7 +1530,7 @@ def _map_color_seg(
             if finite.any():
                 normed[finite] = cmap_params.norm(normed[finite])
             outline_cols = cmap_params.cmap(normed)
-            outline_val_im = map_array(seg.copy(), cell_id, cell_id)
+            outline_val_im = map_array(seg, cell_id, cell_id)
         if seg_erosionpx is not None:
             outline_val_im[
                 outline_val_im == erosion(outline_val_im, footprint_rectangle((seg_erosionpx, seg_erosionpx)))
@@ -1814,8 +1772,12 @@ def _to_hex_no_alpha(color_value: Any) -> str | None:
             if col_to_colorby in adata.obs and hasattr(adata.obs[col_to_colorby], "cat")
             else categories
         )
+        # Map category -> index once (O(K)) instead of a per-category list scan
+        # (was O(K^2) via list.index). all_cats comes from pandas .categories,
+        # which is unique, so a plain dict comprehension is sufficient.
+        cat_to_idx: dict[Any, int] = {c: i for i, c in enumerate(all_cats)}
         for category in categories:
-            idx = all_cats.index(category) if category in all_cats else None
+            idx = cat_to_idx.get(category)
             if idx is not None and idx < len(hex_colors) and hex_colors[idx] is not None:
                 hex_color = hex_colors[idx]
                 assert hex_color is not None  # type narrowing for mypy

tests/pl/test_utils.py

@@ -12,11 +12,10 @@
 from spatialdata.models import PointsModel, ShapesModel, TableModel
 
 import spatialdata_plot
-from spatialdata_plot.pl.render_params import Color
+from spatialdata_plot.pl.render_params import Color, ColorLike
 from spatialdata_plot.pl.utils import (
     _apply_cmap_alpha_to_datashader_result,
     _datashader_map_aggregate_to_color,
-    _get_subplots,
     _set_outline,
     set_zero_in_cmap_to_transparent,
 )
@@ -34,11 +33,6 @@
 #    the comp. function can be accessed as `self.compare(<your_filename>, tolerance=<your_tolerance>)`
 #    ".png" is appended to <your_filename>, no need to set it
 
-# replace with
-# from spatialdata._types import ColorLike
-# once https://github.com/scverse/spatialdata/pull/689/ is in a release
-ColorLike = tuple[float, ...] | str
-
 
 class TestUtils(PlotTester, metaclass=PlotTesterMeta):
     @pytest.mark.parametrize(
@@ -290,23 +284,6 @@ def test_plot_can_handle_rgba_color_specifications(sdata_blobs: SpatialData):
     sdata_blobs.pl.render_shapes(element="blobs_circles", color="blue").pl.show()
 
 
-@pytest.mark.parametrize(
-    "input_output",
-    [
-        (1, 4, 1, [True]),
-        (4, 4, 4, [True, True, True, True]),
-        (6, 4, 8, [True, True, True, True, True, True, False, False]),  # 2 rows with 4 columns
-    ],
-)
-def test_utils_get_subplots_produces_correct_axs_layout(input_output):
-    num_images, ncols, len_axs, axs_visible = input_output
-
-    _, axs = _get_subplots(num_images=num_images, ncols=ncols)
-
-    assert len_axs == len(axs.flatten())
-    assert axs_visible == [ax.axison for ax in axs.flatten()]
-
-
 class TestMultiscaleToSpatialImage:
     """Regression tests for #589: multiscale resolution selection."""