Speeding up projected maps in geoviews?

I’m visualizing public Landsat data with hvplot.xarray but I think this is likely a geoviews issue.

It takes about 3 seconds to render an image when not using the crs:

It takes about 3 minutes to render an image when using the crs:

Without crs it’s using my dask cluster, but with crs it does not use the cluster.

This is the code I’m using:

import rasterio 
import xarray as xr
import hvplot.xarray
from dask.distributed import Client, progress
import cartopy.crs as ccrs

client = Client()

image_url = 'https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/047/027/LC08_L1TP_047027_20130421_20170310_01_T1/LC08_L1TP_047027_20130421_20170310_01_T1_B4.TIF'

da = xr.open_rasterio(image_url, chunks={'band': 1, 'x': 2048, 'y': 2048})

band1 = da.sel(band=1).persist()

display(band1.hvplot(rasterize=True, width=600, height=400, cmap='viridis'))

crs = ccrs.UTM(zone='10n')
display(band1.hvplot(crs=crs, rasterize=True, width=600, height=400, cmap='viridis'))

Here is the notebook.

Is this expected?

Is there anything I can do to speed up visualization with crs specified?

[philippjfr]

I’ve opened https://github.com/pyviz/hvplot/pull/106 to add the project=True/False argument to hvplot.

[philippjfr]

Okay, I’m now wondering whether hvplot is doing something quite suboptimal, using geoviews directly things are pretty quick:

import geoviews as gv
from holoviews.operation.datashader import rasterize
gv.extension('bokeh')

tiff = gv.load_tiff('/Users/philippjfr/Downloads/LC08_L1TP_047027_20130421_20170310_01_T1_B4.TIF')
rasterize(tiff)

It seems like hvplot is projecting before rasterizing, which would explain the slow speed.