Wouter-Michiel Vierdag

Hey @rtubelleza, good spot:) I was aware of this , but didn't have the time yet to actually adjust this and also benchmark the asynchronous rendering that is now in...

One thing to note, we do return a list of arrays: ```python if isinstance(new_raster, (MultiscaleSpatialImage, DataTree)): list_of_xdata = [] for k in new_raster: v = new_raster[k].values() assert len(v) == 1...

One question, are you trying remote reading (as in this is the reason why you implement the LRUstorecache)?

Ok I created some artificial data ``` big_data = np.random.randint(100,55555, size=(1,150000, 150000), dtype=np.uint16) big_image_model = ImageModel2D.parse(big_data, dims=("c","y","x"), scale_factors=[2,2,2,2], chunks=(1,1000,1000)) ``` I add it to an artificial SpatialData object and then...

I would have to see a little bit how this would fit with the `DataTree` which is ultimately what the multiscale images are. I will discuss it with the team....

Thanks for reporting! Will look into it

Yeah I am aware. As far as I know this was already broken in early versions. I take it that this is supposed to clip numerical vectors when coloring? @giovp

Hey @aeisenbarth! Does what @LucaMarconato stated in his comment suit your needs? If so we can close this issue.

Hi @jeffquinn-msk, A few of the issues you are having are seemingly related to parts of napari-core. We have a class called `EventedSet` that could be slowing down things when...

Yes this was indeed related to 'EventedSet' which uses that library