html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue https://github.com/pydata/xarray/pull/4155#issuecomment-674579300,https://api.github.com/repos/pydata/xarray/issues/4155,674579300,MDEyOklzc3VlQ29tbWVudDY3NDU3OTMwMA==,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. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879 https://github.com/pydata/xarray/pull/4155#issuecomment-674578856,https://api.github.com/repos/pydata/xarray/issues/4155,674578856,MDEyOklzc3VlQ29tbWVudDY3NDU3ODg1Ng==,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? ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879 https://github.com/pydata/xarray/pull/4155#issuecomment-674577513,https://api.github.com/repos/pydata/xarray/issues/4155,674577513,MDEyOklzc3VlQ29tbWVudDY3NDU3NzUxMw==,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)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879 https://github.com/pydata/xarray/pull/4155#issuecomment-674319860,https://api.github.com/repos/pydata/xarray/issues/4155,674319860,MDEyOklzc3VlQ29tbWVudDY3NDMxOTg2MA==,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"".","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879 https://github.com/pydata/xarray/pull/4155#issuecomment-674288483,https://api.github.com/repos/pydata/xarray/issues/4155,674288483,MDEyOklzc3VlQ29tbWVudDY3NDI4ODQ4Mw==,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? ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638909879