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plot continous column render labels #443

@ArneDefauw

Description

@ArneDefauw

Rasterizing a labels layer during plotting, can result in removal of small labels, resulting in inconsitenties.

Minimal example to reproduce the issue

import numpy as np
import pandas as pd
from spatialdata.datasets import blobs
from spatialdata.models import TableModel

import spatialdata_plot

RNG = np.random.default_rng(seed=42)

labels_name = "blobs_labels"

sdata_blobs = blobs()

from spatialdata.models import Labels2DModel
import dask.array as da

labels = sdata_blobs[ "blobs_labels" ].data.compute()

# make label 1 small

mask = ( labels == 1 )
labels[ mask ] = 0
labels[ 200, 200  ] = 1

sdata_blobs[ "blobs_labels" ] = Labels2DModel.parse(labels )

If we now do

sdata_blobs.pl.render_labels( "blobs_labels", color="channel_0_sum", table_name="table" ).pl.show()

we get:

Image

which is correct. However, if we do:


arr=da.tile( sdata_blobs[ "blobs_labels" ], ( 4,4 ) )
sdata_blobs[ "blobs_labels_large" ] = Labels2DModel.parse(arr)
adata=sdata_blobs[ "table" ]

adata.obs[ "region" ] = "blobs_labels_large"

adata.uns.pop(TableModel.ATTRS_KEY)

adata = spatialdata.models.TableModel.parse(
    adata,
    region_key="region",
    region="blobs_labels_large",
    instance_key="instance_id",
)
sdata_blobs.pl.render_labels( "blobs_labels_large", color="channel_0_sum", table_name="table" ).pl.show()

we get:

Image

while we expect:

Image

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