Document how to perform on-the-fly normalization for channels. #460

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1. LucaMarconato

   added

   spatialproteomics

   on May 13, 2025
2. MeyerBender commented on May 14, 2025

   

   MeyerBender

   on May 14, 2025

   Contributor

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   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.
3. pakiessling commented on Jul 26, 2025

   

   pakiessling

   on Jul 26, 2025

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   I would also be very much interested in normalization per channel.
   Protein data is super noisy and has vastly different ranges.

   This could maybe be done by supplying a dictionary of channel names and norms?
   I would also like to have an option to specify percentiles a la ("p20","p98") per channel similar to scanpy.pl.umap()

   I will try to see if I can get this to work
4. timtreis commented on Jul 28, 2025

   

   timtreis

   on Jul 28, 2025

   Last edited by timtreis

   Member

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   Would something like this help for your usecase?

   import numpy as np
   import matplotlib.pyplot as plt
   from matplotlib.colors import Normalize
   
   # pick channels and compute per-channel percentiles from the data
   channels = [0, 1, 2]
   arr = sdata.images["image"]  # xarray DataArray with dims ("c", "x", "y")
   
   norms = []
   for c in channels:
       a = arr.sel(c=c).values
       lo = np.nanpercentile(a, 2)
       hi = np.nanpercentile(a, 98)
       norms.append(Normalize(vmin=lo, vmax=hi, clip=True))
   
   sdata.pl.render_images(channel=channels, norm=norms, cmap=[plt.cm.gray]*len(channels)).pl.show()
   
   

   I think this should work, it's just less elegant since the order of the normalisation objects is only implicitly aligned with the order of the channels, not by name or anything like that
5. pakiessling commented on Aug 4, 2025

   

   pakiessling

   on Aug 4, 2025

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   Thanks @timtreis ,

   however this does not seem to be currently supported:

   import matplotlib.pyplot as plt
   from matplotlib.colors import Normalize
   from spatialdata.datasets import blobs
   import spatialdata_plot
   spatialdata_plot.__version__
   >'0.2.10'
   
   sdata = blobs()
   
   # pick channels and compute per-channel percentiles from the data
   channels = [0, 1, 2]
   
   arr = sdata.images["blobs_image"]  # xarray DataArray with dims ("c", "x", "y")
   norms = []
   for c in channels:
       a = arr.sel(c=c).values
       lo = np.nanpercentile(a, 2)
       hi = np.nanpercentile(a, 98)
       norms.append(Normalize(vmin=lo, vmax=hi, clip=True))
   
   sdata.pl.render_images(channel=channels, norm=norms, cmap=[plt.cm.gray] * len(channels)).pl.show()
   
   
   --> 524 params_dict = _validate_image_render_params(
       525     self._sdata,
       526     element=element,
       527     channel=channel,
       528     alpha=alpha,
       529     palette=palette,
   ...
   -> 1621         raise TypeError("Parameter 'norm' must be of type Normalize.")
      1622     if element_type in ["shapes", "points"] and not isinstance(norm, bool | Normalize):
      1623         raise TypeError("Parameter 'norm' must be a boolean or a mpl.Normalize.")
   
   TypeError: Parameter 'norm' must be of type Normalize.
6. timtreis

   added a commit that references this issue on Mar 31, 2026

   Add per-channel norm support for render_images (#460)

   ...

   c764384
7. timtreis

   closed this as completedin #572on Apr 1, 2026