scverse / scverse/spatialdata-plot

render_labels raises IndexError when the table has rows for instances absent from the mask

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Python
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Merged PRs (30d)
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Description

A table may annotate instances that no longer exist in the labels element — e.g. objects lost when segmentation is regenerated, or a table read from an upstream pipeline. render_labels(color=...) then fails instead of treating them as missing.

import anndata as ad, numpy as np, pandas as pd, spatialdata as sd, spatialdata_plot  # noqa
from spatialdata.models import Labels2DModel, TableModel

labels = np.zeros((16, 16), np.uint32)
labels[2:6, 2:6] = 1
labels[8:12, 8:12] = 2

obs = pd.DataFrame({"instance_id": [1, 2, 3], "region": pd.Categorical(["labels"] * 3)})
obs.index = obs.index.astype(str)
table = TableModel.parse(
    ad.AnnData(np.zeros((3, 1), np.float32), obs=obs),
    region="labels", region_key="region", instance_key="instance_id",
)
table.obs["value"] = [1.0, 2.0, 3.0]

sdata = sd.SpatialData(
    labels={"labels": Labels2DModel.parse(labels, dims=("y", "x"))},
    tables={"table": table},
)
sdata.pl.render_labels("labels", color="value", table_name="table").pl.show()
IndexError: boolean index did not match indexed array along axis 0;
size of axis is 2 but size of corresponding boolean axis is 3

The mask is computed from the instance ids present in the labels, but ColorSpec.source_vector/color_vector still have one entry per table row, so ColorSpec.filter indexes a length-2 array with a length-3 mask:

spatialdata_plot/pl/render.py:2409spatialdata_plot/pl/_color.py:791

Rendering without color works, as does dropping the unmatched rows from the table.

spatialdata 0.8.0, spatialdata-plot 0.4.2, Python 3.14.

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Research direction

Run the provided Python reproduction, then inspect spatialdata_plot/pl/render.py around line 2409 and spatialdata_plot/pl/_color.py around line 791. Trace how the labels mask and ColorSpec vectors are filtered; done means render_labels(color=...) handles table rows absent from the labels without IndexError while retaining the existing behavior for matching rows.

Written by the indexing model from the issue text.

Assessment

Tech stack
numpy, pandas, python
Domain
data-visualization
Issue type
Bug
Difficulty
2/5
Estimated time
1-3 hours
Activity status
Active
Clarity
Clearly specified
Newbie friendliness
84/100

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