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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
863119738 https://github.com/pydata/xarray/issues/5434#issuecomment-863119738 https://api.github.com/repos/pydata/xarray/issues/5434 MDEyOklzc3VlQ29tbWVudDg2MzExOTczOA== ghost 10137 2021-06-17T10:20:46Z 2021-06-17T10:26:12Z NONE

Sorry for late response. I was trying to read a big geotif file as follows. import xarray as xr xds = xr.open_rasterio(geotif_file)

My task was to array indexing and to save output into disk.

columns = [8,9,7,100,1050,......, 9000] rows = [18,19,17,1100,1105,......, 9100]

data = xds.isel(x=xr.DataArray(columns), y=xr.DataArray(rows))

np.save('output.npy', data)

Unfortunately, the performance in terms of time requirement seems quite unsatisfactory.

When I saw docs on xr.open_rasterio(), it mentions it is an experimental now. So, I'm curious if it could be much faster when it becomes stable.

I look forward to see it as stable version. Thank you so much.

 
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