Document how to perform on-the-fly normalization for channels.

[LucaMarconato]

Original comment from @MeyerBender

• normalization of channels: see differences between the two notebooks when plotting all channels but DAPI. I guess it is intentional that no normalization is performed by default, and it should be possible to normalize channels using the “norm” argument, but the documentation isn’t very clear on how to do this properly.

The comment refers to the different between making plot with spatialproteomics and making plot with spatialdata-plot.

In spatialmuon we had a preprocessing: Callable argument. Maybe we need something like this here instead of norm? @timtreis

[MeyerBender]

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 range (0-0.05) than the other two (0-1)
img = sdata_blobs.images['blobs_image'].values.copy()
img[0] /= 20
image = sd.models.Image2DModel.parse(
    img, transformations=None, dims=("c", "x", "y"), c_coords=[0, 1, 2]
)
sdata = sd.SpatialData(images={"image": image})

fig, ax = plt.subplots(1, 3)
sdata_blobs.pl.render_images(channel=[0, 1, 2]).pl.show(ax=ax[0])
sdata.pl.render_images(channel=[0, 1, 2]).pl.show(ax=ax[1])

# global normalization
sdata.pl.render_images(channel=[0, 1, 2], norm=Normalize(vmin=img.min(), vmax=img.max())).pl.show(ax=ax[2])

# TODO: what about channel-wise normalization?

It would be nice to have the option to normalize so that the final plot for sdata looks the same as for sdata_blobs.