Matthias Meyer-Bender

Hi, even with h5py==3.10, I still get the same issue. One workaround I could think of is to copy the h5 file each time, however I am unable to figure...

There were two issues with the current methodology which I spotted: 1. The `remove_overlaps` method only looked at the grown masks, but not the original ones. This led to unexpected...

Example code snippet. ``` import spatialdata as sd import spatialdata_plot import matplotlib.pyplot as plt import numpy as np # example data image = sd.models.Image2DModel.parse( np.random.rand(4, 100, 100), transformations=None, dims=("c", "x",...

Example code snippet: ``` import spatialdata as sd import spatialdata_plot from spatialdata.datasets import blobs sdata = blobs() sdata.pl.render_images(channel=0).pl.render_labels(outline_alpha=1, fill_alpha=0).pl.show() ``` For some reason this also creates two colorbars, not sure...

Example code snippet: ``` import spatialdata as sd import spatialdata_plot from spatialdata.datasets import blobs from matplotlib.colors import Normalize sdata_blobs = blobs() # making the first (red) channel have a different...

Example code snippet: ``` import spatialdata as sd import spatialdata_plot from spatialdata.datasets import blobs sdata = blobs() sdata.pl.render_images(channel=0).pl.render_labels(outline_alpha=1, fill_alpha=0).pl.show() ``` It would be cool to have an argument like `contour_color`...