A "close enough" option can take care of issues such as in xarray.where in situations where the coordinates are very close but not exactly matched (here) (e.g. due to Geotif roudning, here ) .

Yes, the alignment stuff in there should do the trick.

if I understand the issue correctly this will be fixed by the index refactor (see https://github.com/pydata/xarray/pull/4489#issuecomment-831809607)

I think it would be nice for xarray to have a "close enough" option where a certain number of decimal places is enough to consider them equal. Is there an issue somewhere where this is under consideration or is there an option already in existence that I missed?

Also related: https://github.com/corteva/rioxarray/discussions/332

Similarly at https://github.com/corteva/rioxarray/issues/298#issuecomment-820557568

python np.abs(resampled.x.values - da2.x.values).max(), np.abs(resampled.y.values - da2.y.values).max() (4.000014541816199e-09, 6.666663665555461e-09)

For the original post for this issue ref: np.abs(raster_in.x.values - out_grid.x.values).max(), np.abs(raster_in.y.values - out_grid.y.values).max() (1.7763568394002505e-15, 7.105427357601002e-15)

Looks like there is a tiny difference in decimal precision.

Similar issue here: https://github.com/corteva/rioxarray/issues/298

Reproducible example in the issue ^^

could you also show raster_in by itself so we can fully reproduce? I was thinking that there were tiny differences in x and y, but python a = xr.open_dataset("out_grid_process_value.nc") b = xr.open_dataset("rasterized_vector.nc") xr.testing.assert_equal(a[["id"]], b) shows that the coordinates are equal so that's not the issue.

Below my code ``` import geopandas import rioxarray from geocube.api.core import make_geocube

Read input_vector and create a GeoDataFrame

vector_in = geopandas.read_file(input_vector)

Read raster data

raster_in = rioxarray.open_rasterio(input_raster)

Create GeoCube

out_grid = make_geocube( vector_data=vector_in, measurements=["id"], like=raster_in, ) out_grid.to_netcdf("rasterized_vector.nc")

Add raster_in to geocube

out_grid["process_value"] = raster_in out_grid.to_netcdf("out_grid_process_value.nc") ``` Here there are two netCDF with output data.

the issue is that the code example you gave both there and here does not directly use xarray so debugging is difficult. Could you post the repr of raster_in and out_grid? If you can, saving both raster_in and out_grid to netCDF and attaching them to a comment might also help.

Without looking at the data I can't confirm, but I suspect this is a alignment issue. If so, we do have a open PR (not merged, yet, unfortunately) which added a tolerance kwarg to align and could fix this.

All do you need is on the first linked issue, so here. You can find QGIS project with data example.

Please could you create a minimally reproducible example, as per the issue template?