issue_comments: 1188529343

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https://github.com/pydata/xarray/issues/2304#issuecomment-1188529343 https://api.github.com/repos/pydata/xarray/issues/2304 1188529343 IC_kwDOAMm_X85G14S_ 145117 2022-07-19T02:35:30Z 2022-07-19T03:20:51Z CONTRIBUTOR

I've run into this issue too, and the xarray decision to use float32 is causing problems. I recognize this is a generic floating-point representation issue, but it could be avoided with float64.

The data value is 1395. The scale is 0.0001.

python val = int(1395) scale = 0.0001 print(val*scale) # 0.1395 print( val * np.array(scale).astype(float) ) # 0.1395 print( val * np.array(scale).astype(np.float16) ) # 0.1395213... print( val * np.array(scale).astype(np.float32) ) # 0.13949999... print( val * np.array(scale).astype(np.float64) ) # 0.1395

Because we are using *1E3 * round(), the difference between 0.1395 and 0.1394999 (or 139.5 and 139.49) ends up being quite large in the downstream product.

 
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