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  • dschwoerer · 2 ✖

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  • Slow performance of isel · 2 ✖

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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
1467929278 https://github.com/pydata/xarray/issues/2227#issuecomment-1467929278 https://api.github.com/repos/pydata/xarray/issues/2227 IC_kwDOAMm_X85XftK- dschwoerer 5637662 2023-03-14T11:32:10Z 2023-03-14T11:32:10Z CONTRIBUTOR

I see, they are not the same - the slow one is still a dask array, the other one is not: Sn (r, theta, phi, sampling) float64 dask.array<chunksize=(14, 52, 2, 10), meta=np.ndarray>, Sn (r, theta, phi, sampling) float64 nan nan nan nan ... nan nan nan Otherwise they are the same, so this might be dask related ...

 
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   Slow performance of isel 331668890
1463894170 https://github.com/pydata/xarray/issues/2227#issuecomment-1463894170 https://api.github.com/repos/pydata/xarray/issues/2227 IC_kwDOAMm_X85XQUCa dschwoerer 5637662 2023-03-10T14:36:43Z 2023-03-10T14:36:43Z CONTRIBUTOR

I just changed theisel = ds[k].isel(**slc, missing_dims="ignore") to: slcp = [slc[d] if d in slc else slice(None) for d in ds[k].dims] theisel = ds[k].values[tuple(slcp)] And that changed the runtime of my code from (unknown, still running after 3 hours) to around 10 seconds.

ds[k] is a 3 dimensional array slc[d] are 7-d numpy array of integers

 
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   Slow performance of isel 331668890

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