I’m struggling to display my integer image 0 values as transparent using rasterize. Looking at the release notes for holoviews=1.14.0 it says this should be possible and refers to Working with Large Data but I still can’t figure it out.
What do I need to modify here?
Full notebook displaying the issue here: https://nbviewer.jupyter.org/gist/rsignell-usgs/a0cce73ae5d8b9b5fa77b836ef2adc08
@philippjfr responded on gitter that we need to do redim.nodata(value=0) here. And yep, that works!
Here’s the full Notebook example
Does anyone know how to use the .redim.nodata(value=0) solution can be applied to a plot with widget selection, e.g. if in the example above da was a stack if some bands and I wanted to call something like
da.hvplot.image(x='x',y='y',groupby='band',widgets={'band':pn.widgets.Select},rasterize=True,data_aspect=1)
How can the redim be applied to the individual images?
I think it’s similar to this:
image, select = ds.hvplot('lon', 'lat', widgets={'time':pn.widgets.Select})
Once widgets is passed, the image is no longer an image type, but a row, so you need to split it out. Once you split it out, you can call redim on image.
Thanks, @ahuang11. I tried to split it out and then can call redim on image.object. But redim doesn’t get applied and interestingly, image has the selection widget embedded. See Screenshot. I also tried a call to da.hvplot() instead of da.hvplot.image() to no avail. Any other thoughts?
That redim does look like it’s working (its at the bottom of the colorbar)
I tried to replicate the data array @rsignell was showing above. Still no luck masking out the nodata values in my plot.
xr.__version__
'0.20.2'
hvplot.__version__
'0.7.3'
I figured out that the issue comes from having named the data array. Then (values=0) needs to be adjusted with (<Name>=0). However, programatically that is hard if the name of a data array changes dynamically as (da.name=0) does not work. Working example with da0.name='Dummy' and hence (Dummy=0) for the redim.
