I don't understand how this fixes the issue.
I looked at the github issue you linked to.
There the problematic statement seems to be a[mask] = a[mask].
In this case is that svat.data[isactive.data] = np.arange(1, index.sum() + 1)?

It seems that the bug is being recognized.
Wouldn't it be better to pin dask to an older version until this issue is resolved?

Dask has slightly different behavior here than numpy. I think we are threading into more obscure features of numpy, of which I'm not certain they are in the scope of Dask or not.

From what I read, the dask developer was surprised a[mask] = b worked in the first place. Which is what we do here. Furthermore, it seems 2025.1.0 also shows some weird behavior with masks. I therefore think it is safer to load these arrays into memory (turning them into regular numpy arrays) for now. That is less hassle then pinning to older versions, as that entails patching the conda-forge repopatches feedstock etc.

Just looked further into this, I think our issue is unrelated to the Dask issue. So what I did never worked with dask in the first place.

import numpy as np

a = np.zeros((3,3))
mask = np.array(
    [
        [True, True, False], 
        [False, False, True], 
        [False, True, False]
    ]
)
n_count = np.sum(mask)
a[mask] = np.arange(1, n_count+1)
print(a) # With numpy it works!

# %%
import dask.array

da = dask.array.from_array(a, chunks=(3,3))
dmask = dask.array.from_array(mask, chunks=(3,3))
da[dmask] = np.arange(1, n_count+1) # Error

Throws error:

ValueError: Boolean index assignment in Dask expects equally shaped arrays.
Example: da1[da2] = da3 where da1.shape == (4,), da2.shape == (4,) and da3.shape == (4,).
Alternatively, you can use the extended API that supportsindexing with tuples.
Example: da1[(da2,)] = da3.

Therefore loading these specific grids into memory is an acceptable, and quickest solution.