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https://github.com/pydata/xarray/issues/1225#issuecomment-326146218 https://api.github.com/repos/pydata/xarray/issues/1225 326146218 MDEyOklzc3VlQ29tbWVudDMyNjE0NjIxOA== 3496314 2017-08-30T23:23:16Z 2017-08-30T23:23:16Z NONE

OK, thanks Joe and Stephan.

On Wed, Aug 30, 2017 at 3:36 PM, Joe Hamman notifications@github.com wrote:

@tbohn https://github.com/tbohn - What is happening here is that xarray is storing the netCDF4 chunk size from the input file. For the LAI variable in your example, that isLAI:_ChunkSizes = 19, 1, 160, 160 ; (you can see this with ncdump -h -s filename.nc).

$ ncdump -s -h veg_hist.0_10n.90_80w.2000_2016.mode_PFT.5dates.nc netcdf veg_hist.0_10n.90_80w.2000_2016.mode_PFT.5dates { dimensions: veg_class = 19 ; lat = 160 ; lon = 160 ; time = UNLIMITED ; // (5 currently) variables: float Cv(veg_class, lat, lon) ; Cv:_FillValue = -1.f ; Cv:units = "-" ; Cv:longname = "Area Fraction" ; Cv:missing_value = -1.f ; Cv:_Storage = "contiguous" ; Cv:_Endianness = "little" ; float LAI(veg_class, time, lat, lon) ; LAI:_FillValue = -1.f ; LAI:units = "m2/m2" ; LAI:longname = "Leaf Area Index" ; LAI:missing_value = -1.f ; LAI:_Storage = "chunked" ; LAI:_ChunkSizes = 19, 1, 160, 160 ; LAI:_Endianness = "little" ; ...

Those integers correspond to the dimensions from LAI. When you slice your dataset, you end up with lat/lon dimensions that are now smaller than the _ChunkSizes. When writing this back to netCDF, xarray is still trying to use the original encoding attribute.

The logical fix is to validate this encoding attribute and either 1) throw an informative error if something isn't going to work, or 2) change the ChunkSizes.

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