Title: Labels categorical coloring fails for label ids missing from table rows when `*_colors` is present in `adata.uns`

[ArneDefauw]

Hi, I was developing a napari plugin that uses napari-spatialdata, and observed a bug in the labels categorical coloring path.

Summary

For labels layers, napari-spatialdata colors by merging the label ids from the layer against a categorical vector from adata.obs.

If some label ids are present in the labels layer but do not have a matching row in the table, and the corresponding *_colors key is present in adata.uns, coloring can fail with:

• DirectLabelColormap validation errors because some color values become NaN
• ValueError: zip() argument 2 is shorter than argument 1 when the stored palette length does not exactly match the category count

Why this looks incorrect

A labels element can legitimately contain ids that are not represented in the table, for example when:

• the segmentation is larger than the annotated subset
• the table only contains active / labeled / filtered instances
• the table comes from a left join against the labels element

In that case, labels coloring should still succeed and give missing ids a fallback color.

Current behavior

In the categorical labels path, napari-spatialdata:

1. takes layer.metadata["indices"]
2. merges those ids against a categorical adata.obs vector
3. builds a category-to-color mapping from adata.uns[f"{key}_colors"] if present
4. maps merged values to colors
5. builds a DirectLabelColormap

If *_colors is absent, a missing-value fallback color is added.

If *_colors is present, no missing fallback is added, so merge-missing rows stay NaN and later become invalid label colors.

Also, the stored palette branch uses strict zipping between categories and colors, so incomplete palettes raise immediately.

Expected behavior

• Missing label ids should get a default “unannotated / missing” color instead of crashing.
• Stored palettes should be handled defensively when their length does not exactly match the category count.

Suggested fix

In the categorical labels coloring path:

• always handle merge-missing values explicitly, even when *_colors already exists in adata.uns
• avoid zip(..., strict=True) for stored palettes, or validate/fallback gracefully before zipping

Environment

Observed with:

• napari-spatialdata 0.7.0
• spatialdata 0.7.2

Minimal repro:

import numpy as np
import pandas as pd
from anndata import AnnData
from napari.utils import DirectLabelColormap

# Simulate a labels layer with ids 1, 2, 3
element_indices = pd.Series([1, 2, 3], name="element_indices")

# Simulate a table that only has rows for ids 1 and 2
adata = AnnData(
    shape=(2, 0),
    obs=pd.DataFrame(
        {
            "instance_id": [1, 2],
            "user_class": pd.Categorical([0, 1], categories=[0, 1]),
        }
    ),
)

# Stored categorical palette is present
adata.uns["user_class_colors"] = np.array(["#80808099", "#ff0000"], dtype=object)

# Same logic as napari-spatialdata categorical labels coloring
vec = adata.obs[["instance_id", "user_class"]].set_index("instance_id")["user_class"]
colors = adata.uns["user_class_colors"]
color_dict = dict(zip(vec.cat.categories, colors.tolist(), strict=True))

merge_df = pd.merge(
    element_indices,
    vec,
    left_on="element_indices",
    right_index=True,
    how="left",
)

merge_df["color"] = merge_df["user_class"].map(color_dict)
print(merge_df)
# element_indices=3 has no table row, so color becomes float NaN

index_color_mapping = dict(zip(merge_df["element_indices"], merge_df["color"], strict=False))
print(index_color_mapping)
# {1: '#80808099', 2: '#ff0000', 3: nan}

DirectLabelColormap(color_dict=index_color_mapping)
# Fails because the color for label id 3 is NaN, not a valid color

I should be able to draft a PR for this issue next week I think