issue_comments: 674577513

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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)

 
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