My testing code:
import spatialdata as sd
import spatialdata_plot
import matplotlib.pyplot as plt
import numpy as np
sdata = sd.datasets.blobs()
sdata.pl.render_labels("blobs_labels", color="channel_0_sum").pl.show()Previously (here I used commit c6575e2) this would look something like this:
However, since e97a7fc, this looks like this:
Just to make sure that the first plot is correct, I extracted the labels where channel_0_sum is high, which shows that the two labels shown in yellow and green in the first plot are indeed the ones with the highest values:
highid = sdata.table.obs[sdata.table[:,'channel_0_sum'].X.toarray() > 1000].instance_id
plt.imshow(np.isin(sdata['blobs_labels'], highid))
My testing code:
Previously (here I used commit c6575e2) this would look something like this:
However, since e97a7fc, this looks like this:
Just to make sure that the first plot is correct, I extracted the labels where
channel_0_sumis high, which shows that the two labels shown in yellow and green in the first plot are indeed the ones with the highest values: