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  • cyhsu · 5 ✖

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  • Implement interp for interpolating between chunks of data (dask) · 5 ✖

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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
674579300 https://github.com/pydata/xarray/pull/4155#issuecomment-674579300 https://api.github.com/repos/pydata/xarray/issues/4155 MDEyOklzc3VlQ29tbWVudDY3NDU3OTMwMA== cyhsu 5323645 2020-08-16T21:18:48Z 2020-08-16T21:48:06Z NONE

Gotcha! Yes, it is. If I have many points in lat, lon, depth, and time, I should better chunk my input arrays at this stage to speed up the performance. The reason why I asked this question is I thought chunking the input array to do the interpolation should faster than if I didn't chunk the input array. But in my test case, it is not. Please see the attached.

The results I show here is the parallel one way slower than the normal case. 

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   Implement interp for interpolating between chunks of data (dask) 638909879
674578856 https://github.com/pydata/xarray/pull/4155#issuecomment-674578856 https://api.github.com/repos/pydata/xarray/issues/4155 MDEyOklzc3VlQ29tbWVudDY3NDU3ODg1Ng== cyhsu 5323645 2020-08-16T21:14:46Z 2020-08-16T21:14:46Z NONE

@pums974 then how about if we do the interpolation by using chunk input array to the chunk interpolated dimension?

 
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   Implement interp for interpolating between chunks of data (dask) 638909879
674577513 https://github.com/pydata/xarray/pull/4155#issuecomment-674577513 https://api.github.com/repos/pydata/xarray/issues/4155 MDEyOklzc3VlQ29tbWVudDY3NDU3NzUxMw== cyhsu 5323645 2020-08-16T21:02:50Z 2020-08-16T21:02:50Z NONE

@fujiisoup Thanks for the response. Since I have not updated my xarray package through this beta version. I hope you can answer my additional question for me. By considering the interpolation, which way is faster? a. chunk the dataset, and then do interpolation or b. chunk the interpolation list and then do interpolation?

a. 

datax = xr.DataArray(data=da.from_array(np.arange(0, 4), chunks=2),
                     coords={"x": np.linspace(0, 1, 4)},
                     dims="x")
datay = xr.DataArray(data=da.from_array(np.arange(0, 4), chunks=2),
                     coords={"y": np.linspace(0, 1, 4)},
                     dims="y")
data = datax * datay

# both of these interp call fails
res = datax.interp(x=np.linspace(0, 1))
print(res.load())

res = data.interp(x=np.linspace(0, 1), y=0.5)
print(res.load())

b. 

datax = xr.DataArray(data=np.arange(0, 4),
                     coords={"x": np.linspace(0, 1, 4)},
                     dims="x")
datay = xr.DataArray(data=np.arange(0, 4),
                     coords={"y": np.linspace(0, 1, 4)},
                     dims="y")
data = datax * datay

x = xr.DataArray(data = da.from_array(np.linspace(0,1), chunks=2), dims='x') res = data.interp(x=x)

 
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   Implement interp for interpolating between chunks of data (dask) 638909879
674319860 https://github.com/pydata/xarray/pull/4155#issuecomment-674319860 https://api.github.com/repos/pydata/xarray/issues/4155 MDEyOklzc3VlQ29tbWVudDY3NDMxOTg2MA== cyhsu 5323645 2020-08-15T00:22:07Z 2020-08-15T00:22:07Z NONE

@fujiisoup Thanks for letting me know. But I am still unable to do even though I have updated my xarray via "conda update xarray".

 
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   Implement interp for interpolating between chunks of data (dask) 638909879
674288483 https://github.com/pydata/xarray/pull/4155#issuecomment-674288483 https://api.github.com/repos/pydata/xarray/issues/4155 MDEyOklzc3VlQ29tbWVudDY3NDI4ODQ4Mw== cyhsu 5323645 2020-08-14T21:57:02Z 2020-08-14T21:57:02Z NONE

Hi Just curious about this. I followed the discussion since this issue addressed. Is this chunk interpolation solved already? 

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   Implement interp for interpolating between chunks of data (dask) 638909879

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