issue_comments

Closing as using mask_and_scale=False produced precise results

Thanks @rabernat I would like to use assert_allclose to test the output but at first pass it seems that might be prohibitively slow to test for large datasets, do you recommend sampling or other good testing strategies (e.g. to assert the xarray datasets are equal to some precision)

@dmedv Thanks for this, it all makes sense to me and I see the same results, however I wasn't able to "convert back" using scale_factor and add_offset ``` from netCDF4 import Dataset

d = Dataset(fileObjs[0]) v = d.variables['analysed_sst']

print("Result with mask_and_scale=True") ds_unchunked = xr.open_dataset(fileObjs[0]) print(ds_unchunked.analysed_sst.sel(lat=slice(20,50),lon=slice(-170,-110)).mean().values)

print("Result with mask_and_scale=False") ds_unchunked = xr.open_dataset(fileObjs[0], mask_and_scale=False) scaled = ds_unchunked.analysed_sst * v.scale_factor + v.add_offset scaled.sel(lat=slice(20,50),lon=slice(-170,-110)).mean().values `` ^^ That returns a different result than what I expect. I wonder if this is because of the_FillValue` missing from trying to convert back.

However this led me to another seemingly related issue: https://github.com/pydata/xarray/issues/2304

Loss of precision seems to be the key here, so coercing the float32s to float64s appears to get the same results from both chunked and unchunked versions - but still not

``` print("results from unchunked dataset") ds_unchunked = xr.open_mfdataset(fileObjs, combine='by_coords') ds_unchunked['analysed_sst'] = ds_unchunked['analysed_sst'].astype(np.float64) print(ds_unchunked.analysed_sst[1,:,:].sel(lat=slice(20,50),lon=slice(-170,-110)).mean().values)

print(f"results from chunked dataset using {chunks}") ds_chunked = xr.open_mfdataset(fileObjs, chunks=chunks, combine='by_coords') ds_chunked['analysed_sst'] = ds_chunked['analysed_sst'].astype(np.float64) print(ds_chunked.analysed_sst[1,:,:].sel(lat=slice(20,50),lon=slice(-170,-110)).mean().values)

print("results from chunked dataset using 'auto'") ds_chunked = xr.open_mfdataset(fileObjs, chunks={'time': 'auto', 'lat': 'auto', 'lon': 'auto'}, combine='by_coords') ds_chunked['analysed_sst'] = ds_chunked['analysed_sst'].astype(np.float64) print(ds_chunked.analysed_sst[1,:,:].sel(lat=slice(20,50),lon=slice(-170,-110)).mean().values) ```

returns: results from unchunked dataset 290.1375818862207 results from chunked dataset using {'time': 1, 'lat': 1799, 'lon': 3600} 290.1375818862207 results from chunked dataset using 'auto' 290.1375818862207